



"""simple docstring"""


import unittest

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

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


if is_torch_available():
				import torch

				from transformers import (
				    YosoForMaskedLM,
				    YosoForMultipleChoice,
				    YosoForQuestionAnswering,
				    YosoForSequenceClassification,
				    YosoForTokenClassification,
				    YosoModel,
				)
				from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Any  ,    lowercase_					:Optional[Any]=13  ,    lowercase_					:Union[str, Any]=7  ,    lowercase_					:Tuple=True  ,    lowercase_					:int=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:str=True  ,    lowercase_					:List[Any]=99  ,    lowercase_					:Any=32  ,    lowercase_					:Any=5  ,    lowercase_					:Any=4  ,    lowercase_					:int=37  ,    lowercase_					:Optional[Any]="gelu"  ,    lowercase_					:List[Any]=0.1  ,    lowercase_					:Any=0.1  ,    lowercase_					:Union[str, Any]=5_12  ,    lowercase_					:Optional[Any]=16  ,    lowercase_					:Dict=2  ,    lowercase_					:Optional[Any]=0.02  ,    lowercase_					:Optional[int]=3  ,    lowercase_					:Dict=4  ,    lowercase_					:Dict=None  ,    )							->     str:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					seq_length
												UpperCAmelCase			 =					is_training
												UpperCAmelCase			 =					use_input_mask
												UpperCAmelCase			 =					use_token_type_ids
												UpperCAmelCase			 =					use_labels
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					hidden_size
												UpperCAmelCase			 =					num_hidden_layers
												UpperCAmelCase			 =					num_attention_heads
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					hidden_dropout_prob
												UpperCAmelCase			 =					attention_probs_dropout_prob
												UpperCAmelCase			 =					max_position_embeddings
												UpperCAmelCase			 =					type_vocab_size
												UpperCAmelCase			 =					type_sequence_label_size
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					num_labels
												UpperCAmelCase			 =					num_choices
												UpperCAmelCase			 =					scope






						def       UpperCAmelCase__	(   self					:Tuple   )							->     int:
												UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size   )

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

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

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

												UpperCAmelCase			 =					self.get_config()

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






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     List[Any]:
												return YosoConfig(
												    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  ,    is_decoder=lowercase_  ,    initializer_range=self.initializer_range  ,    )






						def       UpperCAmelCase__	(   self					:int   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.get_config()
												UpperCAmelCase			 =					3_00
												return config






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[Any]:
												(
												    (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       
												)			 =					self.prepare_config_and_inputs()

												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
												UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    vocab_size=2   )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_mask,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]   )							->     Tuple:
												UpperCAmelCase			 =					YosoModel(config=lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					model(lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_   )
												UpperCAmelCase			 =					model(lowercase_  ,    token_type_ids=lowercase_   )
												UpperCAmelCase			 =					model(lowercase_   )
												self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Dict  ,    lowercase_					:List[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Tuple  ,    lowercase_					:str  ,    lowercase_					:Tuple  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]  ,    )							->     Tuple:
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					YosoModel(lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					model(
												    lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    encoder_hidden_states=lowercase_  ,    encoder_attention_mask=lowercase_  ,    )
												UpperCAmelCase			 =					model(
												    lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    encoder_hidden_states=lowercase_  ,    )
												UpperCAmelCase			 =					model(lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_   )
												self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:List[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:int   )							->     Optional[int]:
												UpperCAmelCase			 =					YosoForMaskedLM(config=lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					model(lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    labels=lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size)   )






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict  ,    lowercase_					:int  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[int]   )							->     int:
												UpperCAmelCase			 =					YosoForQuestionAnswering(config=lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					model(
												    lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    start_positions=lowercase_  ,    end_positions=lowercase_  ,    )
												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       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:int  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Dict   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.num_labels
												UpperCAmelCase			 =					YosoForSequenceClassification(lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					model(lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    labels=lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)   )






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[int]  ,    lowercase_					:int  ,    lowercase_					:int  ,    lowercase_					:str  ,    lowercase_					:int  ,    lowercase_					:Optional[Any]   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.num_labels
												UpperCAmelCase			 =					YosoForTokenClassification(config=lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					model(lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    labels=lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.num_labels)   )






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Tuple  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]  ,    lowercase_					:Tuple  ,    lowercase_					:int  ,    lowercase_					:List[str]  ,    lowercase_					:str   )							->     Tuple:
												UpperCAmelCase			 =					self.num_choices
												UpperCAmelCase			 =					YosoForMultipleChoice(config=lowercase_   )
												model.to(lowercase_   )
												model.eval()
												UpperCAmelCase			 =					input_ids.unsqueeze(1   ).expand(-1  ,    self.num_choices  ,    -1   ).contiguous()
												UpperCAmelCase			 =					token_type_ids.unsqueeze(1   ).expand(-1  ,    self.num_choices  ,    -1   ).contiguous()
												UpperCAmelCase			 =					input_mask.unsqueeze(1   ).expand(-1  ,    self.num_choices  ,    -1   ).contiguous()
												UpperCAmelCase			 =					model(
												    lowercase_  ,    attention_mask=lowercase_  ,    token_type_ids=lowercase_  ,    labels=lowercase_  ,    )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_choices)   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     str:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       
												)			 =					config_and_inputs
												UpperCAmelCase			 =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
												return config, inputs_dict







@require_torch
class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				(
						    (
						        YosoModel,
						        YosoForMaskedLM,
						        YosoForMultipleChoice,
						        YosoForQuestionAnswering,
						        YosoForSequenceClassification,
						        YosoForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False

						__UpperCamelCase													=				()
						__UpperCamelCase													=				(
						    {
						        """feature-extraction""": YosoModel,
						        """fill-mask""": YosoForMaskedLM,
						        """question-answering""": YosoForQuestionAnswering,
						        """text-classification""": YosoForSequenceClassification,
						        """token-classification""": YosoForTokenClassification,
						        """zero-shot""": YosoForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     str:
												UpperCAmelCase			 =					YosoModelTester(self   )
												UpperCAmelCase			 =					ConfigTester(self  ,    config_class=lowercase_  ,    hidden_size=37   )






						def       UpperCAmelCase__	(   self					:int   )							->     List[str]:
												self.config_tester.run_common_tests()






						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_   )






						def       UpperCAmelCase__	(   self					:str   )							->     str:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																		UpperCAmelCase			 =					type
																		self.model_tester.create_and_check_model(*lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*lowercase_   )






						def       UpperCAmelCase__	(   self					:str   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     List[str]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*lowercase_   )






						def       UpperCAmelCase__	(   self					:int   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*lowercase_   )






						@slow
						def       UpperCAmelCase__	(   self					:int   )							->     Union[str, Any]:
												for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase			 =					YosoModel.from_pretrained(lowercase_   )
																		self.assertIsNotNone(lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Dict:
												return







@require_torch
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@slow
						def       UpperCAmelCase__	(   self					:int   )							->     str:
												UpperCAmelCase			 =					YosoModel.from_pretrained('uw-madison/yoso-4096'   )
												UpperCAmelCase			 =					torch.tensor([[0, 1, 2, 3, 4, 5]]   )

												with torch.no_grad():
																		UpperCAmelCase			 =					model(lowercase_   )[0]

												UpperCAmelCase			 =					torch.Size((1, 6, 7_68)   )
												self.assertEqual(output.shape  ,    lowercase_   )

												UpperCAmelCase			 =					torch.tensor(
												    [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]   )

												self.assertTrue(torch.allclose(output[:, :3, :3]  ,    lowercase_  ,    atol=1E-4   )   )






						@slow
						def       UpperCAmelCase__	(   self					:Dict   )							->     Optional[Any]:
												UpperCAmelCase			 =					YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'   )
												UpperCAmelCase			 =					torch.tensor([[0, 1, 2, 3, 4, 5]]   )

												with torch.no_grad():
																		UpperCAmelCase			 =					model(lowercase_   )[0]

												UpperCAmelCase			 =					5_02_65

												UpperCAmelCase			 =					torch.Size((1, 6, vocab_size)   )
												self.assertEqual(output.shape  ,    lowercase_   )

												UpperCAmelCase			 =					torch.tensor(
												    [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]   )

												self.assertTrue(torch.allclose(output[:, :3, :3]  ,    lowercase_  ,    atol=1E-4   )   )






						@slow
						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												UpperCAmelCase			 =					YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'   )
												UpperCAmelCase			 =					torch.arange(40_96   ).unsqueeze(0   )

												with torch.no_grad():
																		UpperCAmelCase			 =					model(lowercase_   )[0]

												UpperCAmelCase			 =					5_02_65

												UpperCAmelCase			 =					torch.Size((1, 40_96, vocab_size)   )
												self.assertEqual(output.shape  ,    lowercase_   )

												UpperCAmelCase			 =					torch.tensor(
												    [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]   )

												self.assertTrue(torch.allclose(output[:, :3, :3]  ,    lowercase_  ,    atol=1E-4   )   )







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







      def lowercase__		( self):
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=_UpperCAmelCase)







      def lowercase__		( self):
            for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=_UpperCAmelCase)







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



