





import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         LiltForQuestionAnswering,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltModel,
     )
     from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST



class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Optional[int]       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   str=13       , UpperCAmelCase_				:   str=7       , UpperCAmelCase_				:   str=True       , UpperCAmelCase_				:   Union[str, Any]=True       , UpperCAmelCase_				:   List[str]=True       , UpperCAmelCase_				:   Tuple=True       , UpperCAmelCase_				:   List[Any]=99       , UpperCAmelCase_				:   Union[str, Any]=24       , UpperCAmelCase_				:   int=2       , UpperCAmelCase_				:   List[Any]=6       , UpperCAmelCase_				:   Tuple=37       , UpperCAmelCase_				:   Tuple="gelu"       , UpperCAmelCase_				:   Dict=0.1       , UpperCAmelCase_				:   Union[str, Any]=0.1       , UpperCAmelCase_				:   Tuple=512       , UpperCAmelCase_				:   Union[str, Any]=16       , UpperCAmelCase_				:   List[Any]=2       , UpperCAmelCase_				:   Tuple=0.02       , UpperCAmelCase_				:   Optional[int]=3       , UpperCAmelCase_				:   Tuple=None       , UpperCAmelCase_				:   int=1_000       , )       ->int:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =parent
   lowerCamelCase__:      Any			  =batch_size
   lowerCamelCase__:      Optional[int]			  =seq_length
   lowerCamelCase__:      Any			  =is_training
   lowerCamelCase__:      Any			  =use_input_mask
   lowerCamelCase__:      Optional[Any]			  =use_token_type_ids
   lowerCamelCase__:      Optional[int]			  =use_labels
   lowerCamelCase__:      str			  =vocab_size
   lowerCamelCase__:      Dict			  =hidden_size
   lowerCamelCase__:      str			  =num_hidden_layers
   lowerCamelCase__:      List[Any]			  =num_attention_heads
   lowerCamelCase__:      Any			  =intermediate_size
   lowerCamelCase__:      Any			  =hidden_act
   lowerCamelCase__:      Optional[int]			  =hidden_dropout_prob
   lowerCamelCase__:      List[Any]			  =attention_probs_dropout_prob
   lowerCamelCase__:      Optional[Any]			  =max_position_embeddings
   lowerCamelCase__:      int			  =type_vocab_size
   lowerCamelCase__:      int			  =type_sequence_label_size
   lowerCamelCase__:      int			  =initializer_range
   lowerCamelCase__:      Optional[int]			  =num_labels
   lowerCamelCase__:      Optional[int]			  =scope
   lowerCamelCase__:      Dict			  =range_bbox

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)

   lowerCamelCase__:      Optional[Any]			  =ids_tensor([self.batch_size, self.seq_length, 4]       , self.range_bbox)
   # Ensure that bbox is legal
   for i in range(bbox.shape[0]):
     for j in range(bbox.shape[1]):
       if bbox[i, j, 3] < bbox[i, j, 1]:
         lowerCamelCase__:      Any			  =bbox[i, j, 3]
         lowerCamelCase__:      Any			  =bbox[i, j, 1]
         lowerCamelCase__:      List[str]			  =t
       if bbox[i, j, 2] < bbox[i, j, 0]:
         lowerCamelCase__:      Dict			  =bbox[i, j, 2]
         lowerCamelCase__:      Any			  =bbox[i, j, 0]
         lowerCamelCase__:      Any			  =t

   lowerCamelCase__:      List[Any]			  =None
   if self.use_input_mask:
     lowerCamelCase__:      Tuple			  =ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2)

   lowerCamelCase__:      List[str]			  =None
   if self.use_token_type_ids:
     lowerCamelCase__:      List[str]			  =ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size)

   lowerCamelCase__:      Dict			  =None
   lowerCamelCase__:      List[Any]			  =None
   if self.use_labels:
     lowerCamelCase__:      Union[str, Any]			  =ids_tensor([self.batch_size]       , self.type_sequence_label_size)
     lowerCamelCase__:      str			  =ids_tensor([self.batch_size, self.seq_length]       , self.num_labels)

   lowerCamelCase__:      List[Any]			  =self.get_config()

   return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[Any]:



   '''simple docstring'''



   return LiltConfig(
       vocab_size=self.vocab_size       , hidden_size=self.hidden_size       , num_hidden_layers=self.num_hidden_layers       , num_attention_heads=self.num_attention_heads       , intermediate_size=self.intermediate_size       , hidden_act=self.hidden_act       , hidden_dropout_prob=self.hidden_dropout_prob       , attention_probs_dropout_prob=self.attention_probs_dropout_prob       , max_position_embeddings=self.max_position_embeddings       , type_vocab_size=self.type_vocab_size       , initializer_range=self.initializer_range       , )

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =LiltModel(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:      List[Any]			  =model(UpperCAmelCase_       , bbox=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_)
   lowerCamelCase__:      int			  =model(UpperCAmelCase_       , bbox=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =model(UpperCAmelCase_       , bbox=UpperCAmelCase_)

   self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))
   self.parent.assertEqual(result.pooler_output.shape       , (self.batch_size, self.hidden_size))

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



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.num_labels
   lowerCamelCase__:      Optional[int]			  =LiltForTokenClassification(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:      Tuple			  =model(
       UpperCAmelCase_       , bbox=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , labels=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   Any       , )       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =LiltForQuestionAnswering(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:      Tuple			  =model(
       UpperCAmelCase_       , bbox=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , start_positions=UpperCAmelCase_       , end_positions=UpperCAmelCase_       , )
   self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length))
   self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length))







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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					
   ):      Optional[int]			  =config_and_inputs
   lowerCamelCase__:      str			  ={
       "input_ids": input_ids,
       "bbox": bbox,
       "token_type_ids": token_type_ids,
       "attention_mask": input_mask,
   }
   return config, inputs_dict



@require_torch
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			(
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 lowercase_		=			(
     {
         "feature-extraction": LiltModel,
         "question-answering": LiltForQuestionAnswering,
         "text-classification": LiltForSequenceClassification,
         "token-classification": LiltForTokenClassification,
         "zero-shot": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase_		=			False
 lowercase_		=			False

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



   '''simple docstring'''



   return True

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =LiltModelTester(self)
   lowerCamelCase__:      int			  =ConfigTester(self       , config_class=UpperCAmelCase_       , hidden_size=37)

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



   '''simple docstring'''



   self.config_tester.run_common_tests()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
     lowerCamelCase__:      int			  =type
     self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_)







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



   '''simple docstring'''



   for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:      int			  =LiltModel.from_pretrained(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)



@require_torch
@slow
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:      str			  =LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =torch.tensor([[1, 2]]       , device=UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]       , device=UpperCAmelCase_)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__:      Optional[Any]			  =model(input_ids=UpperCAmelCase_       , bbox=UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =torch.Size([1, 2, 768])
   lowerCamelCase__:      List[str]			  =torch.tensor(
       [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]       , device=UpperCAmelCase_       , )

   self.assertTrue(outputs.last_hidden_state.shape       , UpperCAmelCase_)
   self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]       , UpperCAmelCase_       , atol=1E-3))








import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			(DDPMParallelScheduler,)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , **UpperCAmelCase_				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  ={
       "num_train_timesteps": 1_000,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "variance_type": "fixed_small",
       "clip_sample": True,
   }

   config.update(**UpperCAmelCase_)
   return config

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[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=UpperCAmelCase_       , beta_end=UpperCAmelCase_)

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



   '''simple docstring'''



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

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



   '''simple docstring'''



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

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Optional[int]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =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_0979)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Tuple			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =self.dummy_model()
   lowerCamelCase__:      int			  =self.dummy_sample_deter
   lowerCamelCase__:      Union[str, Any]			  =self.dummy_sample_deter + 0.1
   lowerCamelCase__:      Optional[Any]			  =self.dummy_sample_deter - 0.1

   lowerCamelCase__:      Optional[Any]			  =samplea.shape[0]
   lowerCamelCase__:      List[Any]			  =torch.stack([samplea, samplea, samplea]       , dim=0)
   lowerCamelCase__:      Union[str, Any]			  =torch.arange(UpperCAmelCase_)[0:3, None].repeat(1       , UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =model(samples.flatten(0       , 1)       , timesteps.flatten(0       , 1))
   lowerCamelCase__:      Tuple			  =scheduler.batch_step_no_noise(UpperCAmelCase_       , timesteps.flatten(0       , 1)       , samples.flatten(0       , 1))

   lowerCamelCase__:      List[str]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Any			  =torch.mean(torch.abs(UpperCAmelCase_))

   assert abs(result_sum.item() - 1153.1833) < 1E-2
   assert abs(result_mean.item() - 0.5005) < 1E-3

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[int]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =self.dummy_model()
   lowerCamelCase__:      List[Any]			  =self.dummy_sample_deter
   lowerCamelCase__:      int			  =torch.manual_seed(0)

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Tuple			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Optional[Any]			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      Any			  =pred_prev_sample

   lowerCamelCase__:      Any			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      List[str]			  =torch.mean(torch.abs(UpperCAmelCase_))

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config(prediction_type="v_prediction")
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      str			  =self.dummy_model()
   lowerCamelCase__:      str			  =self.dummy_sample_deter
   lowerCamelCase__:      Dict			  =torch.manual_seed(0)

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Union[str, Any]			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Dict			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      List[str]			  =pred_prev_sample

   lowerCamelCase__:      List[Any]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Tuple			  =torch.mean(torch.abs(UpperCAmelCase_))

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.scheduler_classes[0]
   lowerCamelCase__:      Union[str, Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      List[Any]			  =[100, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =scheduler.timesteps

   for i, timestep in enumerate(UpperCAmelCase_):
     if i == len(UpperCAmelCase_) - 1:
       lowerCamelCase__:      Dict			  =-1
     else:
       lowerCamelCase__:      Union[str, Any]			  =timesteps[i + 1]

     lowerCamelCase__:      Tuple			  =scheduler.previous_timestep(UpperCAmelCase_)
     lowerCamelCase__:      str			  =prev_t.item()

     self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      List[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Dict			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =[100, 87, 50, 51, 0]

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config()
   lowerCamelCase__:      int			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =[100, 87, 50, 1, 0]
   lowerCamelCase__:      int			  =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 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[Any]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =[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_)

