
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
							    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFSwinForImageClassification,
							    TFSwinForMaskedImageModeling,
							    TFSwinModel,
							)


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor





class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=13 ,	SCREAMING_SNAKE_CASE__=32 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=3 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=[1, 2, 1] ,	SCREAMING_SNAKE_CASE__=[2, 2, 4] ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=2.0 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.02 ,	SCREAMING_SNAKE_CASE__=1E-5 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=10 ,	SCREAMING_SNAKE_CASE__=8 ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			parent
								lowerCAmelCase__ :    int				      =			batch_size
								lowerCAmelCase__ :    Optional[Any]				      =			image_size
								lowerCAmelCase__ :    List[str]				      =			patch_size
								lowerCAmelCase__ :    Any				      =			num_channels
								lowerCAmelCase__ :    str				      =			embed_dim
								lowerCAmelCase__ :    Any				      =			depths
								lowerCAmelCase__ :    Optional[int]				      =			num_heads
								lowerCAmelCase__ :    Optional[int]				      =			window_size
								lowerCAmelCase__ :    Union[str, Any]				      =			mlp_ratio
								lowerCAmelCase__ :    str				      =			qkv_bias
								lowerCAmelCase__ :    List[Any]				      =			hidden_dropout_prob
								lowerCAmelCase__ :    Tuple				      =			attention_probs_dropout_prob
								lowerCAmelCase__ :    List[str]				      =			drop_path_rate
								lowerCAmelCase__ :    Optional[Any]				      =			hidden_act
								lowerCAmelCase__ :    int				      =			use_absolute_embeddings
								lowerCAmelCase__ :    Optional[Any]				      =			patch_norm
								lowerCAmelCase__ :    str				      =			layer_norm_eps
								lowerCAmelCase__ :    List[str]				      =			initializer_range
								lowerCAmelCase__ :    Optional[Any]				      =			is_training
								lowerCAmelCase__ :    Any				      =			scope
								lowerCAmelCase__ :    Optional[Any]				      =			use_labels
								lowerCAmelCase__ :    int				      =			type_sequence_label_size
								lowerCAmelCase__ :    Optional[Any]				      =			encoder_stride
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

								lowerCAmelCase__ :    List[Any]				      =			None
								if self.use_labels:
											lowerCAmelCase__ :    int				      =			ids_tensor([self.batch_size] ,	self.type_sequence_label_size    )

								lowerCAmelCase__ :    str				      =			self.get_config()

								return config, pixel_values, labels
					def lowercase_			(		self    ):
								"""simple docstring"""







								return SwinConfig(
								    image_size=self.image_size ,	patch_size=self.patch_size ,	num_channels=self.num_channels ,	embed_dim=self.embed_dim ,	depths=self.depths ,	num_heads=self.num_heads ,	window_size=self.window_size ,	mlp_ratio=self.mlp_ratio ,	qkv_bias=self.qkv_bias ,	hidden_dropout_prob=self.hidden_dropout_prob ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob ,	drop_path_rate=self.drop_path_rate ,	hidden_act=self.hidden_act ,	use_absolute_embeddings=self.use_absolute_embeddings ,	path_norm=self.patch_norm ,	layer_norm_eps=self.layer_norm_eps ,	initializer_range=self.initializer_range ,	encoder_stride=self.encoder_stride ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			TFSwinModel(config=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[Any]				      =			model(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Union[str, Any]				      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
								lowerCAmelCase__ :    Union[str, Any]				      =			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

								self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, expected_seq_len, expected_dim)    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			TFSwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__    )
								self.parent.assertEqual(
								    result.logits.shape ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

								# test greyscale images
								lowerCAmelCase__ :    List[str]				      =			1
								lowerCAmelCase__ :    Dict				      =			TFSwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Dict				      =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
								lowerCAmelCase__ :    Tuple				      =			model(SCREAMING_SNAKE_CASE__    )
								self.parent.assertEqual(result.logits.shape ,	(self.batch_size, 1, self.image_size, self.image_size)    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Union[str, Any]				      =			self.type_sequence_label_size
								lowerCAmelCase__ :    List[Any]				      =			TFSwinForImageClassification(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Tuple				      =			model(SCREAMING_SNAKE_CASE__ ,	labels=SCREAMING_SNAKE_CASE__    )
								self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.type_sequence_label_size)    )

								# test greyscale images
								lowerCAmelCase__ :    List[Any]				      =			1
								lowerCAmelCase__ :    List[Any]				      =			TFSwinForImageClassification(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
								lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__    )
								self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.type_sequence_label_size)    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			self.prepare_config_and_inputs()
								lowerCAmelCase__ :    List[str]				      =			config_and_inputs
								lowerCAmelCase__ :    Dict				      =			{'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_tf
class 						lowercase       (  __UpperCamelCase	,		__UpperCamelCase	,		unittest.TestCase					):
					__a					=    (
					    (
					        TFSwinModel,
					        TFSwinForImageClassification,
					        TFSwinForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					__a					=    (
					    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
					    if is_tf_available()
					    else {}
					)

					__a					=    False
					__a					=    False
					__a					=    False
					__a					=    False
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			TFSwinModelTester(self    )
								lowerCAmelCase__ :    List[Any]				      =			ConfigTester(self ,	config_class=SCREAMING_SNAKE_CASE__ ,	embed_dim=37    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()
					def lowercase_			(		self    ):
								"""simple docstring"""







								return
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Union[str, Any]				      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__    )
					@unittest.skip(reason='''Swin does not use inputs_embeds'''    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								pass
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    int				      =			model_class(SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(model.get_input_embeddings() ,	tf.keras.layers.Layer    )
											lowerCAmelCase__ :    List[str]				      =			model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__ ,	tf.keras.layers.Dense    )    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Optional[int]				      =			inspect.signature(model.call    )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCAmelCase__ :    Any				      =			[*signature.parameters.keys()]

											lowerCAmelCase__ :    List[str]				      =			['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    Tuple				      =			True

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    str				      =			True
											lowerCAmelCase__ :    Dict				      =			False
											lowerCAmelCase__ :    Optional[int]				      =			True
											lowerCAmelCase__ :    int				      =			model_class(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Dict				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )
											lowerCAmelCase__ :    Tuple				      =			outputs.attentions
											lowerCAmelCase__ :    List[Any]				      =			len(self.model_tester.depths    )
											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											lowerCAmelCase__ :    List[Any]				      =			True
											lowerCAmelCase__ :    str				      =			config.window_size**2
											lowerCAmelCase__ :    Dict				      =			model_class(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    int				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )
											lowerCAmelCase__ :    str				      =			outputs.attentions
											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
											lowerCAmelCase__ :    List[str]				      =			len(SCREAMING_SNAKE_CASE__    )

											# Check attention is always last and order is fine
											lowerCAmelCase__ :    Dict				      =			True
											lowerCAmelCase__ :    List[Any]				      =			True
											lowerCAmelCase__ :    int				      =			model_class(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )

											if hasattr(self.model_tester ,	'''num_hidden_states_types'''    ):
														lowerCAmelCase__ :    Optional[int]				      =			self.model_tester.num_hidden_states_types
											else:
														# also another +1 for reshaped_hidden_states
														lowerCAmelCase__ :    List[str]				      =			2
											self.assertEqual(out_len + added_hidden_states ,	len(SCREAMING_SNAKE_CASE__    )    )

											lowerCAmelCase__ :    str				      =			outputs.attentions

											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			model_class(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			outputs.hidden_states

								lowerCAmelCase__ :    int				      =			getattr(
								    self.model_tester ,	'''expected_num_hidden_layers''' ,	len(self.model_tester.depths    ) + 1    )
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								# Swin has a different seq_length
								lowerCAmelCase__ :    Tuple				      =			to_atuple(config.patch_size    )

								lowerCAmelCase__ :    Tuple				      =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)

								lowerCAmelCase__ :    Union[str, Any]				      =			outputs.reshaped_hidden_states
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Tuple				      =			reshaped_hidden_states[0].shape

								lowerCAmelCase__ :    Optional[int]				      =			tf.reshape(reshaped_hidden_states[0] ,	(batch_size, num_channels, height * width)    )
								lowerCAmelCase__ :    Union[str, Any]				      =			tf.transpose(SCREAMING_SNAKE_CASE__ ,	(0, 2, 1)    )

								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Any				      =			to_atuple(self.model_tester.image_size    )

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    int				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Tuple				      =			True

											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    Any				      =			3

								lowerCAmelCase__ :    str				      =			to_atuple(self.model_tester.image_size    )
								lowerCAmelCase__ :    List[Any]				      =			to_atuple(config.patch_size    )

								lowerCAmelCase__ :    Union[str, Any]				      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCAmelCase__ :    Tuple				      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Optional[int]				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Optional[Any]				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase__ :    str				      =			TFSwinModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )





@require_vision
@require_tf
class 						lowercase       (  unittest.TestCase					):
					@cached_property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return (
								    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    )
								    if is_vision_available()
								    else None
								)
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    )
								lowerCAmelCase__ :    Optional[Any]				      =			self.default_image_processor

								lowerCAmelCase__ :    Any				      =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
								lowerCAmelCase__ :    int				      =			image_processor(images=SCREAMING_SNAKE_CASE__ ,	return_tensors='''tf'''    )

								# forward pass
								lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__    )

								# verify the logits
								lowerCAmelCase__ :    str				      =			tf.TensorShape((1, 1000)    )
								self.assertEqual(outputs.logits.shape ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
								self.assertTrue(np.allclose(outputs.logits[0, :3] ,	SCREAMING_SNAKE_CASE__ ,	atol=1E-4    )    )





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						lowercase       (  __UpperCamelCase					):
					__a					=    (HeunDiscreteScheduler,)
					__a					=    10
					def lowercase_			(		self ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**SCREAMING_SNAKE_CASE__    )
								return config
					def lowercase_			(		self    ):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ ,	beta_end=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ ,	clip_sample=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Optional[Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Optional[int]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    Optional[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    List[Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Tuple				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Any				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    str				      =			self.get_scheduler_config(prediction_type='''v_prediction'''    )
								lowerCAmelCase__ :    Any				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    int				      =			self.dummy_model()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[int]				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			output.prev_sample

								lowerCAmelCase__ :    List[Any]				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Union[str, Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

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

								lowerCAmelCase__ :    Optional[Any]				      =			self.dummy_model()
								lowerCAmelCase__ :    Dict				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Dict				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''cpu'''    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								elif str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002    ) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    str				      =			scheduler_class(**SCREAMING_SNAKE_CASE__ ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__    )

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

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

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

											lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    int				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Dict				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								assert abs(result_sum.item() - 0.00_015    ) < 1E-2
								assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07    ) < 1E-2



