





import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE_  (    __UpperCAmelCase				, unittest.TestCase    ):
						__magic_name__:	Tuple       					=				CodeGenTokenizer
						__magic_name__:	Tuple       					=				CodeGenTokenizerFast
						__magic_name__:	int       					=				True
						__magic_name__:	List[str]       					=				{'''add_prefix_space''': True}
						__magic_name__:	str       					=				False






						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			str:







							"""simple docstring"""


							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_						:	Tuple      =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							    '<|endoftext|>',
							]
							snake_case_						:	List[str]      =		dict(zip(__SCREAMING_SNAKE_CASE						,      range(len(__SCREAMING_SNAKE_CASE		)		)		)		)
							snake_case_						:	Optional[Any]      =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							snake_case_						:	str      =		{'unk_token': '<unk>'}

							snake_case_						:	Optional[Any]      =		os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']		)
							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(__SCREAMING_SNAKE_CASE		) + '\n'		)
							with open(self.merges_file						,      'w'						,      encoding='utf-8'		) as fp:
								fp.write('\n'.join(__SCREAMING_SNAKE_CASE		)		)






						def UpperCAmelCase_ (					self					:					Any						,      **_A					:					Optional[int]		)							->			Optional[Any]:







							"""simple docstring"""


							kwargs.update(self.special_tokens_map		)
							return CodeGenTokenizer.from_pretrained(self.tmpdirname						,      **__SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					Tuple						,      **_A					:					Optional[int]		)							->			str:







							"""simple docstring"""


							kwargs.update(self.special_tokens_map		)
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname						,      **__SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					List[Any]						,      _A					:					str		)							->			Optional[int]:







							"""simple docstring"""


							snake_case_						:	List[str]      =		'lower newer'
							snake_case_						:	Any      =		'lower newer'
							return input_text, output_text






						def UpperCAmelCase_ (					self					:					List[str]		)							->			Dict:







							"""simple docstring"""


							snake_case_						:	int      =		CodeGenTokenizer(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map		)
							snake_case_						:	int      =		'lower newer'
							snake_case_						:	Union[str, Any]      =		['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
							snake_case_						:	Optional[Any]      =		tokenizer.tokenize(__SCREAMING_SNAKE_CASE						,      add_prefix_space=__SCREAMING_SNAKE_CASE		)
							self.assertListEqual(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		)

							snake_case_						:	Union[str, Any]      =		tokens + [tokenizer.unk_token]
							snake_case_						:	List[str]      =		[14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)						,      __SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					int		)							->			Optional[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_						:	Dict      =		self.get_tokenizer()
							snake_case_						:	str      =		self.get_rust_tokenizer(add_prefix_space=__SCREAMING_SNAKE_CASE		)

							snake_case_						:	Union[str, Any]      =		'lower newer'

							# Testing tokenization
							snake_case_						:	Optional[Any]      =		tokenizer.tokenize(__SCREAMING_SNAKE_CASE						,      add_prefix_space=__SCREAMING_SNAKE_CASE		)
							snake_case_						:	str      =		rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE		)
							self.assertListEqual(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		)

							# Testing conversion to ids without special tokens
							snake_case_						:	List[Any]      =		tokenizer.encode(__SCREAMING_SNAKE_CASE						,      add_special_tokens=__SCREAMING_SNAKE_CASE						,      add_prefix_space=__SCREAMING_SNAKE_CASE		)
							snake_case_						:	Optional[Any]      =		rust_tokenizer.encode(__SCREAMING_SNAKE_CASE						,      add_special_tokens=__SCREAMING_SNAKE_CASE		)
							self.assertListEqual(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		)

							# Testing conversion to ids with special tokens
							snake_case_						:	Dict      =		self.get_rust_tokenizer(add_prefix_space=__SCREAMING_SNAKE_CASE		)
							snake_case_						:	Union[str, Any]      =		tokenizer.encode(__SCREAMING_SNAKE_CASE						,      add_prefix_space=__SCREAMING_SNAKE_CASE		)
							snake_case_						:	Any      =		rust_tokenizer.encode(__SCREAMING_SNAKE_CASE		)
							self.assertListEqual(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		)

							# Testing the unknown token
							snake_case_						:	Optional[Any]      =		tokens + [rust_tokenizer.unk_token]
							snake_case_						:	str      =		[14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)						,      __SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					Union[str, Any]						,      *_A					:					Optional[int]						,      **_A					:					Dict		)							->			Dict:







							"""simple docstring"""


							pass






						def UpperCAmelCase_ (					self					:					str						,      _A					:					str=15		)							->			str:







							"""simple docstring"""


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
									snake_case_						:	Dict      =		self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE						,      **__SCREAMING_SNAKE_CASE		)

									# Simple input
									snake_case_						:	int      =		'This is a simple input'
									snake_case_						:	List[str]      =		['This is a simple input 1', 'This is a simple input 2']
									snake_case_						:	Any      =		('This is a simple input', 'This is a pair')
									snake_case_						:	int      =		[
									    ('This is a simple input 1', 'This is a simple input 2'),
									    ('This is a simple pair 1', 'This is a simple pair 2'),
									]

									# Simple input tests
									self.assertRaises(__SCREAMING_SNAKE_CASE						,      tokenizer_r.encode						,      __SCREAMING_SNAKE_CASE						,      max_length=__SCREAMING_SNAKE_CASE						,      padding='max_length'		)

									# Simple input
									self.assertRaises(__SCREAMING_SNAKE_CASE						,      tokenizer_r.encode_plus						,      __SCREAMING_SNAKE_CASE						,      max_length=__SCREAMING_SNAKE_CASE						,      padding='max_length'		)

									# Simple input
									self.assertRaises(
									    __SCREAMING_SNAKE_CASE						,      tokenizer_r.batch_encode_plus						,      __SCREAMING_SNAKE_CASE						,      max_length=__SCREAMING_SNAKE_CASE						,      padding='max_length'						,      )

									# Pair input
									self.assertRaises(__SCREAMING_SNAKE_CASE						,      tokenizer_r.encode						,      __SCREAMING_SNAKE_CASE						,      max_length=__SCREAMING_SNAKE_CASE						,      padding='max_length'		)

									# Pair input
									self.assertRaises(__SCREAMING_SNAKE_CASE						,      tokenizer_r.encode_plus						,      __SCREAMING_SNAKE_CASE						,      max_length=__SCREAMING_SNAKE_CASE						,      padding='max_length'		)

									# Pair input
									self.assertRaises(
									    __SCREAMING_SNAKE_CASE						,      tokenizer_r.batch_encode_plus						,      __SCREAMING_SNAKE_CASE						,      max_length=__SCREAMING_SNAKE_CASE						,      padding='max_length'						,      )






						def UpperCAmelCase_ (					self					:					Any		)							->			str:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		CodeGenTokenizer.from_pretrained(self.tmpdirname						,      pad_token='<pad>'		)

							# Simple input
							snake_case_						:	List[str]      =		'This is a simple input'
							snake_case_						:	Optional[int]      =		['This is a simple input looooooooong', 'This is a simple input']
							snake_case_						:	Dict      =		('This is a simple input', 'This is a pair')
							snake_case_						:	Optional[Any]      =		[
							    ('This is a simple input loooooong', 'This is a simple input'),
							    ('This is a simple pair loooooong', 'This is a simple pair'),
							]

							snake_case_						:	Dict      =		tokenizer.pad_token_id

							snake_case_						:	Optional[int]      =		tokenizer(__SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=30						,      return_tensors='np'		)
							snake_case_						:	str      =		tokenizer(__SCREAMING_SNAKE_CASE						,      padding=__SCREAMING_SNAKE_CASE						,      truncate=__SCREAMING_SNAKE_CASE						,      return_tensors='np'		)
							snake_case_						:	int      =		tokenizer(*__SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=60						,      return_tensors='np'		)
							snake_case_						:	Dict      =		tokenizer(__SCREAMING_SNAKE_CASE						,      padding=__SCREAMING_SNAKE_CASE						,      truncate=__SCREAMING_SNAKE_CASE						,      return_tensors='np'		)

							# s
							# test single string max_length padding
							self.assertEqual(out_s['input_ids'].shape[-1]						,      30		)
							self.assertTrue(pad_token_id in out_s['input_ids']		)
							self.assertTrue(0 in out_s['attention_mask']		)

							# s2
							# test automatic padding
							self.assertEqual(out_sa['input_ids'].shape[-1]						,      33		)
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa['input_ids'][0]		)
							self.assertFalse(0 in out_sa['attention_mask'][0]		)
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa['input_ids'][1]		)
							self.assertTrue(0 in out_sa['attention_mask'][1]		)

							# p
							# test single pair max_length padding
							self.assertEqual(out_p['input_ids'].shape[-1]						,      60		)
							self.assertTrue(pad_token_id in out_p['input_ids']		)
							self.assertTrue(0 in out_p['attention_mask']		)

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa['input_ids'].shape[-1]						,      52		)
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa['input_ids'][0]		)
							self.assertFalse(0 in out_pa['attention_mask'][0]		)
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa['input_ids'][1]		)
							self.assertTrue(0 in out_pa['attention_mask'][1]		)






						def UpperCAmelCase_ (					self					:					List[str]		)							->			List[Any]:







							"""simple docstring"""


							snake_case_						:	Optional[Any]      =		'$$$'
							snake_case_						:	Optional[Any]      =		CodeGenTokenizer.from_pretrained(self.tmpdirname						,      bos_token=__SCREAMING_SNAKE_CASE						,      add_bos_token=__SCREAMING_SNAKE_CASE		)

							snake_case_						:	Tuple      =		'This is a simple input'
							snake_case_						:	Union[str, Any]      =		['This is a simple input 1', 'This is a simple input 2']

							snake_case_						:	int      =		tokenizer.bos_token_id

							snake_case_						:	List[str]      =		tokenizer(__SCREAMING_SNAKE_CASE		)
							snake_case_						:	List[str]      =		tokenizer(__SCREAMING_SNAKE_CASE		)

							self.assertEqual(out_s.input_ids[0]						,      __SCREAMING_SNAKE_CASE		)
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids		)		)

							snake_case_						:	List[Any]      =		tokenizer.decode(out_s.input_ids		)
							snake_case_						:	Dict      =		tokenizer.batch_decode(out_sa.input_ids		)

							self.assertEqual(decode_s.split()[0]						,      __SCREAMING_SNAKE_CASE		)
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa		)		)






						@slow
						def UpperCAmelCase_ (					self					:					Tuple		)							->			str:







							"""simple docstring"""


							snake_case_						:	Any      =		CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'		)

							snake_case_						:	Any      =		'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
							snake_case_						:	int      =		'\nif len_a > len_b:      result = a\nelse:      result = b'

							snake_case_						:	Union[str, Any]      =		tokenizer.encode(__SCREAMING_SNAKE_CASE		)
							snake_case_						:	int      =		['^#', re.escape('<|endoftext|>'		), '^\'\'\'', '^"""', '\n\n\n']
							snake_case_						:	List[Any]      =		tokenizer.decode(__SCREAMING_SNAKE_CASE						,      truncate_before_pattern=__SCREAMING_SNAKE_CASE		)
							self.assertEqual(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		)






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







							"""simple docstring"""


							pass






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						(DDPMParallelScheduler,)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				self.dummy_sample_deter + 0.1
           __a						       =				self.dummy_sample_deter - 0.1

           __a						       =				samplea.shape[0]
           __a						       =				torch.stack([samplea, samplea, samplea]     ,		dim=0)
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				model(samples.flatten(0     ,		1)     ,		timesteps.flatten(0     ,		1))
           __a						       =				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE     ,		timesteps.flatten(0     ,		1)     ,		samples.flatten(0     ,		1))

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

           assert abs(result_sum.item() - 11_53.18_33) < 1E-2
           assert abs(result_mean.item() - 0.50_05) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

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

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

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

               __a						       =				pred_prev_sample

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

           assert abs(result_sum.item() - 2_58.96_06) < 1E-2
           assert abs(result_mean.item() - 0.33_72) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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

           __a						       =				len(__SCREAMING_SNAKE_CASE)

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

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

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

               __a						       =				pred_prev_sample

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

           assert abs(result_sum.item() - 2_02.02_96) < 1E-2
           assert abs(result_mean.item() - 0.26_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

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

           scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)

           __a						       =				scheduler.timesteps

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

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

               self.assertEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

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

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



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]
           __a						       =				len(__SCREAMING_SNAKE_CASE)

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





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[scheduler.config.num_train_timesteps]

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





