

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
     import tensorflow as tf

     from transformers import (
         TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         FlaubertConfig,
         TFFlaubertForMultipleChoice,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForSequenceClassification,
         TFFlaubertForTokenClassification,
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
     )


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       str,)->    Tuple:





        '''simple docstring'''







        A__          =   parent
        A__          =   1_3
        A__          =   7
        A__          =   True
        A__          =   True
        A__          =   True
        A__          =   True
        A__          =   True
        A__          =   False
        A__          =   False
        A__          =   False
        A__          =   2
        A__          =   9_9
        A__          =   0
        A__          =   3_2
        A__          =   2
        A__          =   4
        A__          =   0.1
        A__          =   0.1
        A__          =   5_1_2
        A__          =   1_6
        A__          =   2
        A__          =   0.02
        A__          =   3
        A__          =   4
        A__          =   'last'
        A__          =   True
        A__          =   None
        A__          =   0


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





        '''simple docstring'''







        A__          =   ids_tensor([self.batch_size, self.seq_length],self.vocab_size		)
        A__          =   random_attention_mask([self.batch_size, self.seq_length],dtype=tf.floataa		)

        A__          =   None
        if self.use_input_lengths:
            A__          =   (
                ids_tensor([self.batch_size],vocab_size=2		) + self.seq_length - 2
            )  # small variation of seq_length

        A__          =   None
        if self.use_token_type_ids:
            A__          =   ids_tensor([self.batch_size, self.seq_length],self.n_langs		)

        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],2,dtype=tf.floataa		)
            A__          =   ids_tensor([self.batch_size],self.num_choices		)

        A__          =   FlaubertConfig(
            vocab_size=self.vocab_size,n_special=self.n_special,emb_dim=self.hidden_size,n_layers=self.num_hidden_layers,n_heads=self.num_attention_heads,dropout=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,gelu_activation=self.gelu_activation,sinusoidal_embeddings=self.sinusoidal_embeddings,asm=self.asm,causal=self.causal,n_langs=self.n_langs,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,summary_type=self.summary_type,use_proj=self.use_proj,bos_token_id=self.bos_token_id,)

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )


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





        '''simple docstring'''







        A__          =   TFFlaubertModel(config=lowercase_		)
        A__          =   {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
        A__          =   model(lowercase_		)

        A__          =   [input_ids, input_mask]
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)


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





        '''simple docstring'''







        A__          =   TFFlaubertWithLMHeadModel(lowercase_		)

        A__          =   {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
        A__          =   model(lowercase_		)

        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)		)


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





        '''simple docstring'''







        A__          =   TFFlaubertForQuestionAnsweringSimple(lowercase_		)

        A__          =   {'input_ids': input_ids, 'lengths': input_lengths}

        A__          =   model(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 						snake_case__     (      self :       Tuple,lowercase_ :       List[Any],lowercase_ :       Optional[int],lowercase_ :       Optional[int],lowercase_ :       List[Any],lowercase_ :       Tuple,lowercase_ :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Dict,lowercase_ :       Any,)->    str:





        '''simple docstring'''







        A__          =   TFFlaubertForSequenceClassification(lowercase_		)

        A__          =   {'input_ids': input_ids, 'lengths': input_lengths}

        A__          =   model(lowercase_		)

        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)


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





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   TFFlaubertForTokenClassification(config=lowercase_		)
        A__          =   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       Dict,lowercase_ :       int,lowercase_ :       int,lowercase_ :       Dict,lowercase_ :       Optional[Any],lowercase_ :       str,lowercase_ :       str,lowercase_ :       Optional[int],lowercase_ :       Optional[Any],)->    Dict:





        '''simple docstring'''







        A__          =   self.num_choices
        A__          =   TFFlaubertForMultipleChoice(config=lowercase_		)
        A__          =   tf.tile(tf.expand_dims(lowercase_,1		),(1, self.num_choices, 1)		)
        A__          =   tf.tile(tf.expand_dims(lowercase_,1		),(1, self.num_choices, 1)		)
        A__          =   tf.tile(tf.expand_dims(lowercase_,1		),(1, self.num_choices, 1)		)
        A__          =   {
            'input_ids': multiple_choice_inputs_ids,
            'attention_mask': multiple_choice_input_mask,
            'token_type_ids': multiple_choice_token_type_ids,
        }
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)		)


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        (
            (
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						
        )          =   config_and_inputs
        A__          =   {
            'input_ids': input_ids,
            'token_type_ids': token_type_ids,
            'langs': token_type_ids,
            'lengths': input_lengths,
        }
        return config, inputs_dict




@require_tf
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase			=							(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    lowerCamelCase			=							(
        {
            'feature-extraction': TFFlaubertModel,
            'fill-mask': TFFlaubertWithLMHeadModel,
            'question-answering': TFFlaubertForQuestionAnsweringSimple,
            'text-classification': TFFlaubertForSequenceClassification,
            'token-classification': TFFlaubertForTokenClassification,
            'zero-shot': TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowerCamelCase			=							False
    lowerCamelCase			=							False


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





        '''simple docstring'''







        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith('Fast'		)
        ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

        return False


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





        '''simple docstring'''







        A__          =   TFFlaubertModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,emb_dim=3_7		)


    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        self.config_tester.run_common_tests()


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*lowercase_		)


    def 						snake_case__     (      self :       int		)->    List[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    str:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase_		)


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





        '''simple docstring'''







        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__          =   TFFlaubertModel.from_pretrained(lowercase_		)
            self.assertIsNotNone(lowercase_		)




@require_tf
@require_sentencepiece
@require_tokenizers
class  A						(							unittest.TestCase ):







    """simple docstring"""





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





        '''simple docstring'''







        A__          =   TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'		)

        A__          =   tf.convert_to_tensor(
            [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]],dtype=tf.intaa,)  # "J'aime flaubert !"

        A__          =   model(lowercase_		)[0]
        A__          =   tf.TensorShape((1, 8, 5_1_2)		)
        self.assertEqual(output.shape,lowercase_		)
        # compare the actual values for a slice.
        A__          =   tf.convert_to_tensor(
            [
                [
                    [-1.8_768_773, -1.566_555, 0.27_072_418],
                    [-1.6_920_038, -0.5_873_505, 1.9_329_599],
                    [-2.9_563_985, -1.6_993_835, 1.7_972_052],
                ]
            ],dtype=tf.floataa,)

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(),expected_slice.numpy(),atol=1E-4		)		)




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(DPMSolverSinglestepScheduler,)
    lowerCamelCase			=							(('num_inference_steps', 25),)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'prediction_type': 'epsilon',
            'thresholding': False,
            'sample_max_value': 1.0,
            'algorithm_type': 'dpmsolver++',
            'solver_type': 'midpoint',
            'lambda_min_clipped': -float('inf'		),
            'variance_type': None,
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=0,**lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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


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





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any]=0,**lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

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

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


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Optional[int]=None,**lowercase_ :       int		)->    int:





        '''simple docstring'''







        if scheduler is None:
            A__          =   self.scheduler_classes[0]
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)

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

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

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

        return sample


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   5_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_574		) < 1E-3


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





        '''simple docstring'''







        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


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





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3

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

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

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        self.check_over_configs(thresholding=lowercase_		)
        for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
                for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            thresholding=lowercase_,prediction_type=lowercase_,sample_max_value=lowercase_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
                for order in [1, 2, 3]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        A__          =   self.full_loop(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"


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





        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowercase_		)
        self.check_over_configs(lower_order_final=lowercase_		)


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





        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


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





        '''simple docstring'''







        self.check_over_configs(variance_type=lowercase_		)
        self.check_over_configs(variance_type='learned_range'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=lowercase_,time_step=0		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_248		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction'		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.1_453		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.0_649		) < 1E-3


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase_		)

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

        assert sample.dtype == torch.floataa

