



"""simple docstring"""


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 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							CodeGenTokenizer
						__UpperCamelCase		    =							CodeGenTokenizerFast
						__UpperCamelCase		    =							True
						__UpperCamelCase		    =							{"""add_prefix_space""": True}
						__UpperCamelCase		    =							False






						def      UpperCAmelCase__					( self			:List[Any] )    ->      str:
												super().setUp()

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

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






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  **lowercase_			:List[Any] )    ->      Optional[Any]:
												kwargs.update(self.special_tokens_map )
												return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  **lowercase_			:Dict )    ->      str:
												kwargs.update(self.special_tokens_map )
												return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:str       ,  lowercase_			:Tuple )    ->      str:
												UpperCAmelCase					=			'lower newer'
												UpperCAmelCase					=			'lower newer'
												return input_text, output_text






						def      UpperCAmelCase__					( self			:List[str] )    ->      List[Any]:
												UpperCAmelCase					=			CodeGenTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map )
												UpperCAmelCase					=			'lower newer'
												UpperCAmelCase					=			['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
												UpperCAmelCase					=			tokenizer.tokenize(lowercase_       ,  add_prefix_space=lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )

												UpperCAmelCase					=			tokens + [tokenizer.unk_token]
												UpperCAmelCase					=			[14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ )       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Any:
												if not self.test_rust_tokenizer:
																		return

												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_rust_tokenizer(add_prefix_space=lowercase_ )

												UpperCAmelCase					=			'lower newer'

												# Testing tokenization
												UpperCAmelCase					=			tokenizer.tokenize(lowercase_       ,  add_prefix_space=lowercase_ )
												UpperCAmelCase					=			rust_tokenizer.tokenize(lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )

												# Testing conversion to ids without special tokens
												UpperCAmelCase					=			tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_       ,  add_prefix_space=lowercase_ )
												UpperCAmelCase					=			rust_tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )

												# Testing conversion to ids with special tokens
												UpperCAmelCase					=			self.get_rust_tokenizer(add_prefix_space=lowercase_ )
												UpperCAmelCase					=			tokenizer.encode(lowercase_       ,  add_prefix_space=lowercase_ )
												UpperCAmelCase					=			rust_tokenizer.encode(lowercase_ )
												self.assertListEqual(lowercase_       ,  lowercase_ )

												# Testing the unknown token
												UpperCAmelCase					=			tokens + [rust_tokenizer.unk_token]
												UpperCAmelCase					=			[14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase_ )       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  *lowercase_			:Optional[int]       ,  **lowercase_			:int )    ->      Dict:
												# It's very difficult to mix/test pretokenization with byte-level
												# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
												pass






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Any=15 )    ->      str:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
																								UpperCAmelCase					=			self.rust_tokenizer_class.from_pretrained(lowercase_       ,  **lowercase_ )

																								# Simple input
																								UpperCAmelCase					=			'This is a simple input'
																								UpperCAmelCase					=			['This is a simple input 1', 'This is a simple input 2']
																								UpperCAmelCase					=			('This is a simple input', 'This is a pair')
																								UpperCAmelCase					=			[
																								    ('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(lowercase_       ,  tokenizer_r.encode       ,  lowercase_       ,  max_length=lowercase_       ,  padding='max_length' )

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

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

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

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

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






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      List[str]:
												UpperCAmelCase					=			CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  pad_token='<pad>' )

												# Simple input
												UpperCAmelCase					=			'This is a simple input'
												UpperCAmelCase					=			['This is a simple input looooooooong', 'This is a simple input']
												UpperCAmelCase					=			('This is a simple input', 'This is a pair')
												UpperCAmelCase					=			[
												    ('This is a simple input loooooong', 'This is a simple input'),
												    ('This is a simple pair loooooong', 'This is a simple pair'),
												]

												UpperCAmelCase					=			tokenizer.pad_token_id

												UpperCAmelCase					=			tokenizer(lowercase_       ,  padding='max_length'       ,  max_length=30       ,  return_tensors='np' )
												UpperCAmelCase					=			tokenizer(lowercase_       ,  padding=lowercase_       ,  truncate=lowercase_       ,  return_tensors='np' )
												UpperCAmelCase					=			tokenizer(*lowercase_       ,  padding='max_length'       ,  max_length=60       ,  return_tensors='np' )
												UpperCAmelCase					=			tokenizer(lowercase_       ,  padding=lowercase_       ,  truncate=lowercase_       ,  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			:int )    ->      List[Any]:
												UpperCAmelCase					=			'$$$'
												UpperCAmelCase					=			CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=lowercase_       ,  add_bos_token=lowercase_ )

												UpperCAmelCase					=			'This is a simple input'
												UpperCAmelCase					=			['This is a simple input 1', 'This is a simple input 2']

												UpperCAmelCase					=			tokenizer.bos_token_id

												UpperCAmelCase					=			tokenizer(lowercase_ )
												UpperCAmelCase					=			tokenizer(lowercase_ )

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

												UpperCAmelCase					=			tokenizer.decode(out_s.input_ids )
												UpperCAmelCase					=			tokenizer.batch_decode(out_sa.input_ids )

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






						@slow
						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )

												UpperCAmelCase					=			'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
												UpperCAmelCase					=			'\nif len_a > len_b:      result = a\nelse:      result = b'

												UpperCAmelCase					=			tokenizer.encode(lowercase_ )
												UpperCAmelCase					=			['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
												UpperCAmelCase					=			tokenizer.decode(lowercase_       ,  truncate_before_pattern=lowercase_ )
												self.assertEqual(lowercase_       ,  lowercase_ )






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










"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							(UniPCMultistepScheduler,)
						__UpperCamelCase		    =							(("""num_inference_steps""", 25),)






						def      UpperCAmelCase__					( self			:Any       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												UpperCAmelCase					=			{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'solver_order': 2,
												    'solver_type': 'bh2',
												}

												config.update(**lowercase_ )
												return config






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[int]=0       ,  **lowercase_			:Optional[Any] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )
																		# copy over dummy past residuals
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(lowercase_ )
																								UpperCAmelCase					=			scheduler_class.from_pretrained(lowercase_ )
																								new_scheduler.set_timesteps(lowercase_ )
																								# copy over dummy past residuals
																								UpperCAmelCase					=			dummy_past_residuals[: new_scheduler.config.solver_order]

																		UpperCAmelCase				,   UpperCAmelCase					=			sample, sample
																		for t in range(lowercase_       ,  time_step + scheduler.config.solver_order + 1 ):
																								UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																								UpperCAmelCase					=			new_scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

																								assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Dict=0       ,  **lowercase_			:Optional[int] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )

																		# copy over dummy past residuals (must be after setting timesteps)
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(lowercase_ )
																								UpperCAmelCase					=			scheduler_class.from_pretrained(lowercase_ )
																								# copy over dummy past residuals
																								new_scheduler.set_timesteps(lowercase_ )

																								# copy over dummy past residual (must be after setting timesteps)
																								UpperCAmelCase					=			dummy_past_residuals[: new_scheduler.config.solver_order]

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			new_scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

																		assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Optional[int]=None       ,  **lowercase_			:str )    ->      Dict:
												if scheduler is None:
																		UpperCAmelCase					=			self.scheduler_classes[0]
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter
												scheduler.set_timesteps(lowercase_ )

												for i, t in enumerate(scheduler.timesteps ):
																		UpperCAmelCase					=			model(lowercase_       ,  lowercase_ )
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_ ).prev_sample

												return sample






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )

												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		UpperCAmelCase					=			self.dummy_sample
																		UpperCAmelCase					=			0.1 * sample

																		if num_inference_steps is not None and hasattr(lowercase_       ,  'set_timesteps' ):
																								scheduler.set_timesteps(lowercase_ )
																		elif num_inference_steps is not None and not hasattr(lowercase_       ,  'set_timesteps' ):
																								UpperCAmelCase					=			num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

																		UpperCAmelCase					=			scheduler.timesteps[5]
																		UpperCAmelCase					=			scheduler.timesteps[6]

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												UpperCAmelCase					=			UniPCMultistepScheduler(**self.get_scheduler_config() )
												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3

												UpperCAmelCase					=			DPMSolverSinglestepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			DEISMultistepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			DPMSolverMultistepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			UniPCMultistepScheduler.from_config(scheduler.config )

												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      str:
												self.check_over_configs(thresholding=lowercase_ )
												for order in [1, 2, 3]:
																		for solver_type in ["bh1", "bh2"]:
																								for threshold in [0.5, 1.0, 2.0]:
																														for prediction_type in ["epsilon", "sample"]:
																																				self.check_over_configs(
																																				    thresholding=lowercase_       ,  prediction_type=lowercase_       ,  sample_max_value=lowercase_       ,  solver_order=lowercase_       ,  solver_type=lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Any )    ->      str:
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Optional[Any]:
												for solver_type in ["bh1", "bh2"]:
																		for order in [1, 2, 3]:
																								for prediction_type in ["epsilon", "sample"]:
																														self.check_over_configs(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														UpperCAmelCase					=			self.full_loop(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






						def      UpperCAmelCase__					( self			:Dict )    ->      List[Any]:
												self.check_over_configs(lower_order_final=lowercase_ )
												self.check_over_configs(lower_order_final=lowercase_ )






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																		self.check_over_forward(num_inference_steps=lowercase_       ,  time_step=0 )






						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.full_loop()
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												UpperCAmelCase					=			self.full_loop(prediction_type='v_prediction' )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.1014 ) < 1E-3






						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(thresholding=lowercase_       ,  dynamic_thresholding_ratio=0 )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter.half()
												scheduler.set_timesteps(lowercase_ )

												for i, t in enumerate(scheduler.timesteps ):
																		UpperCAmelCase					=			model(lowercase_       ,  lowercase_ )
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_ ).prev_sample

												assert sample.dtype == torch.floataa






						def      UpperCAmelCase__					( self			:List[Any]       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		scheduler.set_timesteps(scheduler.config.num_train_timesteps )
																		assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps





