




"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerModel,
				)
				from transformers.models.roformer.modeling_tf_roformer import (
				    TFRoFormerSelfAttention,
				    TFRoFormerSinusoidalPositionalEmbedding,
				)




class   a__			:





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




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  parent
											_lowerCAmelCase:int									=  13
											_lowerCAmelCase:Dict									=  7
											_lowerCAmelCase:Any									=  True
											_lowerCAmelCase:Any									=  True
											_lowerCAmelCase:int									=  True
											_lowerCAmelCase:Any									=  True
											_lowerCAmelCase:Optional[Any]									=  99
											_lowerCAmelCase:str									=  32
											_lowerCAmelCase:Any									=  2
											_lowerCAmelCase:Optional[int]									=  4
											_lowerCAmelCase:Union[str, Any]									=  37
											_lowerCAmelCase:List[Any]									=  '''gelu'''
											_lowerCAmelCase:Optional[Any]									=  0.1
											_lowerCAmelCase:List[str]									=  0.1
											_lowerCAmelCase:Optional[Any]									=  512
											_lowerCAmelCase:List[str]									=  16
											_lowerCAmelCase:Union[str, Any]									=  2
											_lowerCAmelCase:int									=  0.02
											_lowerCAmelCase:Optional[Any]									=  3
											_lowerCAmelCase:int									=  4
											_lowerCAmelCase:Union[str, Any]									=  None





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:str									=  ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

											_lowerCAmelCase:Optional[Any]									=  None
											if self.use_input_mask:
																_lowerCAmelCase:Optional[Any]									=  random_attention_mask([self.batch_size, self.seq_length])

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

											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:Any									=  None
											_lowerCAmelCase:int									=  None
											if self.use_labels:
																_lowerCAmelCase:Tuple									=  ids_tensor([self.batch_size]     ,self.type_sequence_label_size)
																_lowerCAmelCase:Optional[Any]									=  ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels)
																_lowerCAmelCase:List[Any]									=  ids_tensor([self.batch_size]     ,self.num_choices)

											_lowerCAmelCase:Any									=  RoFormerConfig(
											    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     ,return_dict=a__     ,)

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





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Tuple     ,a__      :	Tuple     ,a__      :	int     ,a__      :	Union[str, Any]     ,a__      :	Optional[Any]     ,a__      :	Tuple     ,a__      :	List[Any])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  TFRoFormerModel(config=a__)
											_lowerCAmelCase:Any									=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

											_lowerCAmelCase:Union[str, Any]									=  [input_ids, input_mask]
											_lowerCAmelCase:Optional[Any]									=  model(a__)

											_lowerCAmelCase:Tuple									=  model(a__)

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





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




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  True
											_lowerCAmelCase:Any									=  TFRoFormerForCausalLM(config=a__)
											_lowerCAmelCase:List[str]									=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}
											_lowerCAmelCase:List[Any]									=  model(a__)['''logits''']
											self.parent.assertListEqual(
											    list(prediction_scores.numpy().shape)     ,[self.batch_size, self.seq_length, self.vocab_size])





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[str]     ,a__      :	Tuple     ,a__      :	List[Any]     ,a__      :	int     ,a__      :	Union[str, Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  TFRoFormerForMaskedLM(config=a__)
											_lowerCAmelCase:List[str]									=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}
											_lowerCAmelCase:str									=  model(a__)
											self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Any     ,a__      :	List[Any]     ,a__      :	Tuple     ,a__      :	Any     ,a__      :	Optional[int]     ,a__      :	str     ,a__      :	str)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.num_labels
											_lowerCAmelCase:Tuple									=  TFRoFormerForSequenceClassification(config=a__)
											_lowerCAmelCase:Union[str, Any]									=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}

											_lowerCAmelCase:Optional[int]									=  model(a__)
											self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Optional[int]     ,a__      :	Optional[Any]     ,a__      :	List[str]     ,a__      :	Union[str, Any]     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.num_choices
											_lowerCAmelCase:Dict									=  TFRoFormerForMultipleChoice(config=a__)
											_lowerCAmelCase:List[str]									=  tf.tile(tf.expand_dims(a__     ,1)     ,(1, self.num_choices, 1))
											_lowerCAmelCase:str									=  tf.tile(tf.expand_dims(a__     ,1)     ,(1, self.num_choices, 1))
											_lowerCAmelCase:Optional[Any]									=  tf.tile(tf.expand_dims(a__     ,1)     ,(1, self.num_choices, 1))
											_lowerCAmelCase:Optional[int]									=  {
											    '''input_ids''': multiple_choice_inputs_ids,
											    '''attention_mask''': multiple_choice_input_mask,
											    '''token_type_ids''': multiple_choice_token_type_ids,
											}
											_lowerCAmelCase:List[str]									=  model(a__)
											self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_choices))





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




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.num_labels
											_lowerCAmelCase:List[Any]									=  TFRoFormerForTokenClassification(config=a__)
											_lowerCAmelCase:Dict									=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}
											_lowerCAmelCase:Union[str, Any]									=  model(a__)
											self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels))





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




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  TFRoFormerForQuestionAnswering(config=a__)
											_lowerCAmelCase:Union[str, Any]									=  {
											    '''input_ids''': input_ids,
											    '''attention_mask''': input_mask,
											    '''token_type_ids''': token_type_ids,
											}

											_lowerCAmelCase:Optional[int]									=  model(a__)
											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      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  self.prepare_config_and_inputs()
											(
											    (
											    _lowerCAmelCase
											)							,   (
											    _lowerCAmelCase
											)							,   (
											    _lowerCAmelCase
											)							,   (
											    _lowerCAmelCase
											)							,   (
											    _lowerCAmelCase
											)							,   (
											    _lowerCAmelCase
											)							,   (
											    _lowerCAmelCase
											)							,   
											):Union[str, Any]									=  config_and_inputs
											_lowerCAmelCase:int									=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
											return config, inputs_dict




@require_tf
class   a__			(       UpperCamelCase_					,	UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = (
						    (
						        TFRoFormerModel,
						        TFRoFormerForCausalLM,
						        TFRoFormerForMaskedLM,
						        TFRoFormerForQuestionAnswering,
						        TFRoFormerForSequenceClassification,
						        TFRoFormerForTokenClassification,
						        TFRoFormerForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						snake_case__             = (
						    {
						        '''feature-extraction''': TFRoFormerModel,
						        '''fill-mask''': TFRoFormerForMaskedLM,
						        '''question-answering''': TFRoFormerForQuestionAnswering,
						        '''text-classification''': TFRoFormerForSequenceClassification,
						        '''text-generation''': TFRoFormerForCausalLM,
						        '''token-classification''': TFRoFormerForTokenClassification,
						        '''zero-shot''': TFRoFormerForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						snake_case__             = False
						snake_case__             = False





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[Any]     ,a__      :	List[Any]     ,a__      :	List[str]     ,a__      :	int     ,a__      :	List[str])						->       Tuple:




											"""simple docstring"""







											if pipeline_test_casse_name == "TextGenerationPipelineTests":
																return True

											return False





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




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  TFRoFormerModelTester(self)
											_lowerCAmelCase:Dict									=  ConfigTester(self     ,config_class=a__     ,hidden_size=37)





						def       __UpperCamelCase     (							self      :	List[Any])						->       Tuple:




											"""simple docstring"""







											self.config_tester.run_common_tests()





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




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:str									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_lm_head(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:int									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:str									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*a__)







						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''')
											self.assertIsNotNone(a__)




@require_tf
class   a__			(       unittest.TestCase  ):





						@slow
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''')
											_lowerCAmelCase:List[Any]									=  tf.constant([[0, 1, 2, 3, 4, 5]])
											_lowerCAmelCase:Tuple									=  model(a__)[0]

											# TODO Replace vocab size
											_lowerCAmelCase:Any									=  5_0000

											_lowerCAmelCase:Any									=  [1, 6, vocab_size]
											self.assertEqual(output.shape     ,a__)

											print(output[:, :3, :3])

											# TODO Replace values below with what was printed above.
											_lowerCAmelCase:List[Any]									=  tf.constant(
											    [
											        [
											            [-0.12053341, -1.0264901, 0.29221946],
											            [-1.5133783, 0.197433, 0.15190607],
											            [-5.0135403, -3.900256, -0.84038764],
											        ]
											    ])
											tf.debugging.assert_near(output[:, :3, :3]     ,a__     ,atol=1E-4)




@require_tf
class   a__			(       unittest.TestCase  ):
						snake_case__             = 1E-4





						def       __UpperCamelCase     (							self      :	int)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:str									=  tf.constant([[4, 10]])
											_lowerCAmelCase:List[Any]									=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,embedding_dim=6)

											_lowerCAmelCase:Union[str, Any]									=  emba(input_ids.shape)
											_lowerCAmelCase:str									=  tf.constant(
											    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]])

											tf.debugging.assert_near(a__     ,a__     ,atol=self.tolerance)







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




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  tf.constant(
											    [
											        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
											        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
											        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
											    ])
											_lowerCAmelCase:str									=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=512     ,embedding_dim=512)
											emba([2, 16, 512])
											_lowerCAmelCase:str									=  emba.weight[:3, :5]

											tf.debugging.assert_near(a__     ,a__     ,atol=self.tolerance)




@require_tf
class   a__			(       unittest.TestCase  ):
						snake_case__             = 1E-4





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




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  tf.reshape(tf.range(2 * 12 * 16 * 64     ,dtype=tf.floataa)     ,shape=(2, 12, 16, 64)) / 100

											_lowerCAmelCase:List[Any]									=  -tf.reshape(tf.range(2 * 12 * 16 * 64     ,dtype=tf.floataa)     ,shape=(2, 12, 16, 64)) / 100

											_lowerCAmelCase:Dict									=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=32     ,embedding_dim=64)
											_lowerCAmelCase:Tuple									=  embed_positions([2, 16, 768])[None, None, :, :]

											_lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  TFRoFormerSelfAttention.apply_rotary_position_embeddings(
											    a__     ,a__     ,a__)

											_lowerCAmelCase:Tuple									=  tf.constant(
											    [
											        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
											        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
											        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
											        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
											        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
											        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
											    ])
											_lowerCAmelCase:Optional[Any]									=  tf.constant(
											    [
											        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
											        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
											        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
											        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
											        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
											        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
											    ])

											tf.debugging.assert_near(query_layer[0, 0, :6, :8]     ,a__     ,atol=self.tolerance)
											tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,a__     ,atol=self.tolerance)












"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   a__			(       UpperCamelCase_  ):
						snake_case__             = (KDPMaDiscreteScheduler,)
						snake_case__             = 1_0





						def       __UpperCamelCase     (							self      :	Dict     ,**a__      :	str)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''num_train_timesteps''': 1100,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**a__)
											return config





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




											"""simple docstring"""







											for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=a__)





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




											"""simple docstring"""







											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=a__     ,beta_end=a__)





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




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.scheduler_classes[0]
											_lowerCAmelCase:Union[str, Any]									=  self.get_scheduler_config(prediction_type='''v_prediction''')
											_lowerCAmelCase:Tuple									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Optional[Any]									=  self.dummy_model()
											_lowerCAmelCase:List[Any]									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:Optional[Any]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Any									=  model(a__     ,a__)

																_lowerCAmelCase:Union[str, Any]									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Optional[int]									=  output.prev_sample

											_lowerCAmelCase:str									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Union[str, Any]									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
																assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:Tuple									=  self.scheduler_classes[0]
											_lowerCAmelCase:List[str]									=  self.get_scheduler_config()
											_lowerCAmelCase:Union[str, Any]									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Any									=  self.dummy_model()
											_lowerCAmelCase:str									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:List[str]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:List[str]									=  model(a__     ,a__)

																_lowerCAmelCase:int									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:List[Any]									=  output.prev_sample

											_lowerCAmelCase:Tuple									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Dict									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3







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




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:str									=  self.scheduler_classes[0]
											_lowerCAmelCase:Optional[Any]									=  self.get_scheduler_config()
											_lowerCAmelCase:Any									=  scheduler_class(**a__)

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

											_lowerCAmelCase:Optional[int]									=  self.dummy_model()
											_lowerCAmelCase:Optional[Any]									=  self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																_lowerCAmelCase:Union[str, Any]									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Tuple									=  model(a__     ,a__)

																_lowerCAmelCase:str									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Dict									=  output.prev_sample

											_lowerCAmelCase:Any									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:str									=  torch.mean(torch.abs(a__))

											if str(a__).startswith('''cpu'''):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3






