



import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     DistilBertForMaskedLM,
     DistilBertForMultipleChoice,
     DistilBertForQuestionAnswering,
     DistilBertForSequenceClassification,
     DistilBertForTokenClassification,
     DistilBertModel,
 )

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=13     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=7     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Dict=True     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     str=99     ,		__SCREAMING_SNAKE_CASE							:     Any=32     ,		__SCREAMING_SNAKE_CASE							:     List[str]=5     ,		__SCREAMING_SNAKE_CASE							:     Tuple=4     ,		__SCREAMING_SNAKE_CASE							:     Tuple=37     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     str=0.1     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Dict=16     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     int=3     ,		__SCREAMING_SNAKE_CASE							:     List[str]=4     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		):


           '''simple docstring'''

           __a						       =				parent
           __a						       =				batch_size
           __a						       =				seq_length
           __a						       =				is_training
           __a						       =				use_input_mask
           __a						       =				use_token_type_ids
           __a						       =				use_labels
           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				intermediate_size
           __a						       =				hidden_act
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				type_sequence_label_size
           __a						       =				initializer_range
           __a						       =				num_labels
           __a						       =				num_choices
           __a						       =				scope



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size)

           __a						       =				None
           if self.use_input_mask:
               __a						       =				random_attention_mask([self.batch_size, self.seq_length])

           __a						       =				None
           __a						       =				None
           __a						       =				None
           if self.use_labels:
               __a						       =				ids_tensor([self.batch_size]     ,		self.type_sequence_label_size)
               __a						       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels)
               __a						       =				ids_tensor([self.batch_size]     ,		self.num_choices)

           __a						       =				self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           return DistilBertConfig(
               vocab_size=self.vocab_size     ,		dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		hidden_dim=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		)



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				DistilBertModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				DistilBertForMaskedLM(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size))



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				DistilBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(
               __SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		start_positions=__SCREAMING_SNAKE_CASE     ,		end_positions=__SCREAMING_SNAKE_CASE)
           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 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				self.num_labels
           __a						       =				DistilBertForSequenceClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels))



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


           '''simple docstring'''

           __a						       =				self.num_labels
           __a						       =				DistilBertForTokenClassification(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels))



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				self.num_choices
           __a						       =				DistilBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				input_ids.unsqueeze(1).expand(-1     ,		self.num_choices     ,		-1).contiguous()
           __a						       =				input_mask.unsqueeze(1).expand(-1     ,		self.num_choices     ,		-1).contiguous()
           __a						       =				model(
               __SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE     ,		)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices))





       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           ((__a)       ,   (__a)       ,   (__a)       ,   (__a)       ,   (__a)       ,   (__a))						       =				config_and_inputs
           __a						       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict


@require_torch
class     _A       (	__UpperCAmelCase      ,__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Optional[int]											=						(
           (
               DistilBertModel,
               DistilBertForMaskedLM,
               DistilBertForMultipleChoice,
               DistilBertForQuestionAnswering,
               DistilBertForSequenceClassification,
               DistilBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       UpperCamelCase__  :    List[str]											=						(
           {
               '''feature-extraction''': DistilBertModel,
               '''fill-mask''': DistilBertForMaskedLM,
               '''question-answering''': DistilBertForQuestionAnswering,
               '''text-classification''': DistilBertForSequenceClassification,
               '''token-classification''': DistilBertForTokenClassification,
               '''zero-shot''': DistilBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase__  :    str											=						True
       UpperCamelCase__  :    List[Any]											=						True
       UpperCamelCase__  :    Dict											=						True
       UpperCamelCase__  :    Union[str, Any]											=						True



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				DistilBertModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__SCREAMING_SNAKE_CASE     ,		dim=37)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_model(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_masked_lm(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_question_answering(*__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_sequence_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_token_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_multiple_choice(*__SCREAMING_SNAKE_CASE)



       @slow
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a						       =				DistilBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       @slow
       @require_torch_gpu
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               # BertForMultipleChoice behaves incorrectly in JIT environments.
               if model_class == DistilBertForMultipleChoice:
                   return

               __a						       =				True
               __a						       =				model_class(config=__SCREAMING_SNAKE_CASE)

               __a						       =				self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				torch.jit.trace(
                   __SCREAMING_SNAKE_CASE     ,		(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

               with tempfile.TemporaryDirectory() as tmp:
                   torch.jit.save(__SCREAMING_SNAKE_CASE     ,		os.path.join(__SCREAMING_SNAKE_CASE     ,		'''traced_model.pt'''))
                   __a						       =				torch.jit.load(os.path.join(__SCREAMING_SNAKE_CASE     ,		'''traced_model.pt''')     ,		map_location=__SCREAMING_SNAKE_CASE)
                   loaded(inputs_dict['''input_ids'''].to(__SCREAMING_SNAKE_CASE)     ,		inputs_dict['''attention_mask'''].to(__SCREAMING_SNAKE_CASE))


@require_torch
class     _A       (	unittest.TestCase    ):



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


           '''simple docstring'''

           __a						       =				DistilBertModel.from_pretrained('''distilbert-base-uncased''')
           __a						       =				torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]])
           __a						       =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)[0]
           __a						       =				torch.Size((1, 11, 768))
           self.assertEqual(output.shape     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				torch.tensor(
               [[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]])

           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-4))










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)





