



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
						import tensorflow as tf

						from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a=3,			__a=32,			__a=3,			__a=10,			__a=[10, 20, 30, 40],			__a=[1, 1, 2, 1],			__a=True,			__a=True,			__a="relu",			__a=3,			__a=None,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    parent
														_lowerCAmelCase     :							Optional[Any]    =    batch_size
														_lowerCAmelCase     :							Optional[Any]    =    image_size
														_lowerCAmelCase     :							str    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embeddings_size
														_lowerCAmelCase     :							Dict    =    hidden_sizes
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Any    =    is_training
														_lowerCAmelCase     :							Tuple    =    use_labels
														_lowerCAmelCase     :							Union[str, Any]    =    hidden_act
														_lowerCAmelCase     :							List[Any]    =    num_labels
														_lowerCAmelCase     :							Any    =    scope
														_lowerCAmelCase     :							int    =    len(__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

														_lowerCAmelCase     :							Tuple    =    None
														if self.use_labels:
																					_lowerCAmelCase     :							Tuple    =    ids_tensor([self.batch_size],			self.num_labels)

														_lowerCAmelCase     :							int    =    self.get_config()
														return config, pixel_values, labels







							def    snake_case__      (   self):

														'''simple docstring'''

														return RegNetConfig(
														    num_channels=self.num_channels,			embeddings_size=self.embeddings_size,			hidden_sizes=self.hidden_sizes,			depths=self.depths,			hidden_act=self.hidden_act,			num_labels=self.num_labels,			)







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    TFRegNetModel(config=__a)
														_lowerCAmelCase     :							Any    =    model(__a,			training=__a)
														# expected last hidden states: B, C, H // 32, W // 32
														self.parent.assertEqual(
														    result.last_hidden_state.shape,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),			)







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.num_labels
														_lowerCAmelCase     :							Optional[Any]    =    TFRegNetForImageClassification(__a)
														_lowerCAmelCase     :							Optional[Any]    =    model(__a,			labels=__a,			training=__a)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.prepare_config_and_inputs()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    config_and_inputs
														_lowerCAmelCase     :							Union[str, Any]    =    {"pixel_values": pixel_values}
														return config, inputs_dict



@require_tf
class   UpperCAmelCase_    (       a   ,					a   ,					unittest.TestCase):
							lowerCamelCase__   			=		(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
							lowerCamelCase__   			=		(
							    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    TFRegNetModelTester(self)
														_lowerCAmelCase     :							Optional[Any]    =    ConfigTester(self,			config_class=__a,			has_text_modality=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														return







							@unittest.skip(reason="RegNet does not use inputs_embeds")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,			reason="TF does not support backprop for grouped convolutions on CPU.",			)
							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														super().test_keras_fit()







							@unittest.skip(reason="RegNet does not support input and output embeddings")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowerCAmelCase     :							List[Any]    =    model_class(__a)
																					_lowerCAmelCase     :							Dict    =    inspect.signature(model.call)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					_lowerCAmelCase     :							str    =    [*signature.parameters.keys()]

																					_lowerCAmelCase     :							List[Any]    =    ["pixel_values"]
																					self.assertListEqual(arg_names[:1],			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														def check_hidden_states_output(__a,			__a,			__a):
																					_lowerCAmelCase     :							Any    =    model_class(__a)
																					_lowerCAmelCase     :							Optional[int]    =    model(**self._prepare_for_class(__a,			__a),			training=__a)

																					_lowerCAmelCase     :							Any    =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					_lowerCAmelCase     :							Optional[int]    =    self.model_tester.num_stages
																					self.assertEqual(len(__a),			expected_num_stages + 1)

																					# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]),			[self.model_tester.image_size // 2, self.model_tester.image_size // 2],			)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs_for_common()
														_lowerCAmelCase     :							str    =    ["basic", "bottleneck"]
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												_lowerCAmelCase     :							str    =    layer_type
																												_lowerCAmelCase     :							Optional[Any]    =    True
																												check_hidden_states_output(__a,			__a,			__a)

																												# check that output_hidden_states also work using config
																												del inputs_dict["output_hidden_states"]
																												_lowerCAmelCase     :							Optional[Any]    =    True

																												check_hidden_states_output(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs_for_common()

														def check_equivalence(__a,			__a,			__a,			__a={}):
																					_lowerCAmelCase     :							Union[str, Any]    =    model(__a,			return_dict=__a,			**__a)
																					_lowerCAmelCase     :							Any    =    model(__a,			return_dict=__a,			**__a).to_tuple()

																					def recursive_check(__a,			__a):
																												if isinstance(__a,			(List, Tuple)):
																																			for tuple_iterable_value, dict_iterable_value in zip(__a,			__a):
																																										recursive_check(__a,			__a)
																												elif tuple_object is None:
																																			return
																												else:
																																			self.assertTrue(
																																			    all(tf.equal(__a,			__a)),			msg=(
																																			        "Tuple and dict output are not equal. Difference:"
																																			        f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
																																			    ),			)

																												recursive_check(__a,			__a)

														for model_class in self.all_model_classes:
																					_lowerCAmelCase     :							str    =    model_class(__a)

																					_lowerCAmelCase     :							int    =    self._prepare_for_class(__a,			__a)
																					_lowerCAmelCase     :							int    =    self._prepare_for_class(__a,			__a)
																					check_equivalence(__a,			__a,			__a)

																					_lowerCAmelCase     :							Any    =    self._prepare_for_class(__a,			__a,			return_labels=__a)
																					_lowerCAmelCase     :							Optional[int]    =    self._prepare_for_class(__a,			__a,			return_labels=__a)
																					check_equivalence(__a,			__a,			__a)

																					_lowerCAmelCase     :							List[str]    =    self._prepare_for_class(__a,			__a)
																					_lowerCAmelCase     :							List[Any]    =    self._prepare_for_class(__a,			__a)
																					check_equivalence(__a,			__a,			__a,			{"output_hidden_states": True})

																					_lowerCAmelCase     :							Tuple    =    self._prepare_for_class(__a,			__a,			return_labels=__a)
																					_lowerCAmelCase     :							List[Any]    =    self._prepare_for_class(__a,			__a,			return_labels=__a)
																					check_equivalence(__a,			__a,			__a,			{"output_hidden_states": True})







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowerCAmelCase     :							int    =    TFRegNetModel.from_pretrained(__a)
																					self.assertIsNotNone(__a)






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							return image



@require_tf
@require_vision
class   UpperCAmelCase_    (       unittest.TestCase):







							@cached_property
							def    snake_case__      (   self):

														'''simple docstring'''

														return (
														    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
														    if is_vision_available()
														    else None
														)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])

														_lowerCAmelCase     :							Optional[Any]    =    self.default_image_processor
														_lowerCAmelCase     :							Dict    =    prepare_img()
														_lowerCAmelCase     :							List[Any]    =    image_processor(images=__a,			return_tensors="tf")

														# forward pass
														_lowerCAmelCase     :							Optional[int]    =    model(**__a,			training=__a)

														# verify the logits
														_lowerCAmelCase     :							Dict    =    tf.TensorShape((1, 1000))
														self.assertEqual(outputs.logits.shape,			__a)

														_lowerCAmelCase     :							int    =    tf.constant([-0.4_180, -1.5_051, -3.4_836])

														tf.debugging.assert_near(outputs.logits[0, :3],			__a,			atol=1E-4)




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Any      =(DPMSolverSinglestepScheduler,)
      UpperCamelCase__							:  Tuple      =(('num_inference_steps', 25),)






      def        lowerCamelCase       (					self						:     Optional[Any]			,   **lowercase_						:     List[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 '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        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[Any]=0			,   **lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Optional[Any]		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Dict		=0.1 * sample
             _lowerCamelCase       :		Optional[int]		=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                    _lowerCamelCase	,     _lowerCamelCase       :		Any		=sample, sample
                    for t in range(lowercase_			,   time_step + scheduler.config.solver_order + 1 ):
                           _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                           _lowerCamelCase       :		Optional[Any]		=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        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=0			,   **lowercase_						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		int		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Dict		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Any		=0.1 * sample
             _lowerCamelCase       :		int		=[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config()
                    _lowerCamelCase       :		Any		=scheduler_class(**lowercase_ )
                    scheduler.set_timesteps(lowercase_ )

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

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Optional[int]		=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)
                           _lowerCamelCase       :		str		=dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase       :		Dict		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                    _lowerCamelCase       :		Dict		=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        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]=None			,   **lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             if scheduler is None:
                    _lowerCamelCase       :		Tuple		=self.scheduler_classes[0]
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		List[Any]		=self.scheduler_classes[0]
             _lowerCamelCase       :		Optional[Any]		=self.get_scheduler_config(**lowercase_ )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		str		=10
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_model()
             _lowerCamelCase       :		Dict		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		List[str]		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Optional[int]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             return sample






      def        lowerCamelCase       (					self						:     Dict )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		int		=50
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		Tuple		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

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

             _lowerCamelCase       :		int		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2574 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		Optional[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		Tuple		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3

             _lowerCamelCase       :		Dict		=DEISMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Optional[int]		=DPMSolverMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Union[str, Any]		=UniPCMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		List[str]		=DPMSolverSinglestepScheduler.from_config(scheduler.config )

             _lowerCamelCase       :		List[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """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        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """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_			,   )
                                         _lowerCamelCase       :		Any		=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        lowerCamelCase       (					self						:     List[Any] )					->						List[Any]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=lowercase_			,   time_step=0 )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.full_loop()
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2248 ) < 1E-3






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.full_loop(prediction_type='v_prediction' )
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.1453 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(prediction_type='v_prediction'			,   use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.0649 ) < 1E-3






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.scheduler_classes[0]
             _lowerCamelCase       :		List[str]		=self.get_scheduler_config(thresholding=lowercase_			,   dynamic_thresholding_ratio=0 )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=10
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		List[Any]		=self.dummy_sample_deter.half()
             scheduler.set_timesteps(lowercase_ )

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

             assert sample.dtype == torch.floataa


