import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MaskFormerSwinBackbone
							from transformers.models.maskformer import MaskFormerSwinModel


class 						lowercase    :




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Dict=13	,							_UpperCamelCase   :					Any=32	,							_UpperCamelCase   :					str=2	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					Optional[Any]=16	,							_UpperCamelCase   :					int=[1, 2, 1]	,							_UpperCamelCase   :					Union[str, Any]=[2, 2, 4]	,							_UpperCamelCase   :					Optional[int]=2	,							_UpperCamelCase   :					int=2.0	,							_UpperCamelCase   :					List[str]=True	,							_UpperCamelCase   :					str=0.0	,							_UpperCamelCase   :					Union[str, Any]=0.0	,							_UpperCamelCase   :					List[str]=0.1	,							_UpperCamelCase   :					List[Any]="gelu"	,							_UpperCamelCase   :					str=False	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					int=0.0_2	,							_UpperCamelCase   :					List[Any]=1e-5	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Tuple=None	,							_UpperCamelCase   :					Tuple=True	,							_UpperCamelCase   :					List[str]=10	,							_UpperCamelCase   :					Dict=8	,							_UpperCamelCase   :					List[Any]=["stage1", "stage2", "stage3"]	,							_UpperCamelCase   :					List[Any]=[1, 2, 3]	,							)					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       patch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       embed_dim
												SCREAMING_SNAKE_CASE    			=       depths
												SCREAMING_SNAKE_CASE    			=       num_heads
												SCREAMING_SNAKE_CASE    			=       window_size
												SCREAMING_SNAKE_CASE    			=       mlp_ratio
												SCREAMING_SNAKE_CASE    			=       qkv_bias
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       drop_path_rate
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       use_absolute_embeddings
												SCREAMING_SNAKE_CASE    			=       patch_norm
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       scope
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       encoder_stride
												SCREAMING_SNAKE_CASE    			=       out_features
												SCREAMING_SNAKE_CASE    			=       out_indices




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )

												SCREAMING_SNAKE_CASE    			=       self.get_config()

												return config, pixel_values, labels




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



												'''simple docstring'''

												return MaskFormerSwinConfig(
												    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	,							out_features=self.out_features	,							out_indices=self.out_indices	,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MaskFormerSwinModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MaskFormerSwinBackbone(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps      )	,							len(config.out_features      )      )
												self.parent.assertListEqual(list(result.feature_maps[0].shape      )	,							[13, 16, 16, 16]      )

												# verify channels
												self.parent.assertEqual(len(model.channels      )	,							len(config.out_features      )      )
												self.parent.assertListEqual(model.channels	,							[16, 32, 64]      )

												# verify ValueError
												with self.parent.assertRaises(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       ["stem"]
																	SCREAMING_SNAKE_CASE    			=       MaskFormerSwinBackbone(config=_UpperCamelCase      )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       List[Any]								=							(
							    (
							        MaskFormerSwinModel,
							        MaskFormerSwinBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__						:       Optional[Any]								=							{"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
							lowercase__						:       Optional[Any]								=							False
							lowercase__						:       List[Any]								=							False
							lowercase__						:       Optional[Any]								=							False
							lowercase__						:       List[str]								=							False
							lowercase__						:       Optional[int]								=							False




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MaskFormerSwinModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							embed_dim=37      )




							@require_torch_multi_gpu
							@unittest.skip(
							    reason=(
							        "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
							        " `nn.DataParallel`"
							    )      )
							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												pass




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



												'''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 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												return




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*_UpperCamelCase      )




							@unittest.skip("Swin does not use inputs_embeds"      )
							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												pass




							@unittest.skip("Swin does not support feedforward chunking"      )
							def 	__snake_case(						self   :					Tuple      )					->				List[str]:



												'''simple docstring'''

												pass




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	self.assertIsInstance(model.get_input_embeddings()	,							(nn.Module)      )
																	SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_UpperCamelCase	,							nn.Linear      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       inspect.signature(model.forward      )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	SCREAMING_SNAKE_CASE    			=       [*signature.parameters.keys()]

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




							@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions"      )
							def 	__snake_case(						self   :					List[str]      )					->				int:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"      )
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )      )

												SCREAMING_SNAKE_CASE    			=       outputs.hidden_states

												SCREAMING_SNAKE_CASE    			=       getattr(
												    self.model_tester	,							"expected_num_hidden_layers"	,							len(self.model_tester.depths      ) + 1      )
												self.assertEqual(len(_UpperCamelCase      )	,							_UpperCamelCase      )

												# Swin has a different seq_length
												SCREAMING_SNAKE_CASE    			=       (
												    config.patch_size
												    if isinstance(config.patch_size	,							collections.abc.Iterable      )
												    else (config.patch_size, config.patch_size)
												)

												SCREAMING_SNAKE_CASE    			=       (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]	,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												SCREAMING_SNAKE_CASE    			=       (
												    self.model_tester.image_size
												    if isinstance(self.model_tester.image_size	,							collections.abc.Iterable      )
												    else (self.model_tester.image_size, self.model_tester.image_size)
												)

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       True
																	self.check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE    			=       True

																	self.check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE    			=       3

												SCREAMING_SNAKE_CASE    			=       (
												    self.model_tester.image_size
												    if isinstance(self.model_tester.image_size	,							collections.abc.Iterable      )
												    else (self.model_tester.image_size, self.model_tester.image_size)
												)
												SCREAMING_SNAKE_CASE    			=       (
												    config.patch_size
												    if isinstance(config.patch_size	,							collections.abc.Iterable      )
												    else (config.patch_size, config.patch_size)
												)

												SCREAMING_SNAKE_CASE    			=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
												SCREAMING_SNAKE_CASE    			=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       True
																	self.check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							(padded_height, padded_width)      )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE    			=       True
																	self.check_hidden_states_output(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							(padded_height, padded_width)      )




							@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints"      )
							def 	__snake_case(						self   :					List[Any]      )					->				List[Any]:



												'''simple docstring'''

												pass




							@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"      )
							def 	__snake_case(						self   :					Union[str, Any]      )					->				Optional[Any]:



												'''simple docstring'''

												pass




							@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"      )
							def 	__snake_case(						self   :					Any      )					->				Tuple:



												'''simple docstring'''

												pass





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												def set_nan_tensor_to_zero(_UpperCamelCase   :					List[str]      ):
																	SCREAMING_SNAKE_CASE    			=       0
																	return t

												def check_equivalence(_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[int]={}      ):
																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase	,							return_dict=_UpperCamelCase	,							**_UpperCamelCase      )
																						SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase	,							return_dict=_UpperCamelCase	,							**_UpperCamelCase      ).to_tuple()

																						def recursive_check(_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					str      ):
																											if isinstance(_UpperCamelCase	,							(List, Tuple)      ):
																																for tuple_iterable_value, dict_iterable_value in zip(_UpperCamelCase	,							_UpperCamelCase      ):
																																					recursive_check(_UpperCamelCase	,							_UpperCamelCase      )
																											elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																																for tuple_iterable_value, dict_iterable_value in zip(
																																    tuple_object.values()	,							dict_object.values()      ):
																																					recursive_check(_UpperCamelCase	,							_UpperCamelCase      )
																											elif tuple_object is None:
																																return
																											else:
																																self.assertTrue(
																																    torch.allclose(
																																        set_nan_tensor_to_zero(_UpperCamelCase      )	,							set_nan_tensor_to_zero(_UpperCamelCase      )	,							atol=1e-5      )	,							msg=(
																																        "Tuple and dict output are not equal. Difference:"
																																        F" {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:"
																																        F" {torch.isnan(_UpperCamelCase      ).any()} and `inf`: {torch.isinf(_UpperCamelCase      )}. Dict has"
																																        F" `nan`: {torch.isnan(_UpperCamelCase      ).any()} and `inf`: {torch.isinf(_UpperCamelCase      )}."
																																    )	,							)

																						recursive_check(_UpperCamelCase	,							_UpperCamelCase      )

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	model.to(_UpperCamelCase      )
																	model.eval()

																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )
																	check_equivalence(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )
																	check_equivalence(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase      )
																	check_equivalence(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							{"output_hidden_states": True}      )

																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )
																	check_equivalence(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase	,							{"output_hidden_states": True}      )



@require_torch
class 						lowercase    (     unittest.TestCase				,       a     ):
							lowercase__						:       Union[str, Any]								=							(MaskFormerSwinBackbone,) if is_torch_available() else ()
							lowercase__						:       List[str]								=							MaskFormerSwinConfig




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MaskFormerSwinModelTester(self      )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE    			=       inputs_dict["pixel_values"].shape[0]

												for backbone_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       backbone_class(_UpperCamelCase      )
																	backbone.to(_UpperCamelCase      )
																	backbone.eval()

																	SCREAMING_SNAKE_CASE    			=       backbone(**_UpperCamelCase      )

																	# Test default outputs and verify feature maps
																	self.assertIsInstance(outputs.feature_maps	,							_UpperCamelCase      )
																	self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
																	for feature_map, n_channels in zip(outputs.feature_maps	,							backbone.channels      ):
																						self.assertTrue(feature_map.shape[:2]	,							(batch_size, n_channels)      )
																	self.assertIsNone(outputs.hidden_states      )
																	self.assertIsNone(outputs.attentions      )

																	# Test output_hidden_states=True
																	SCREAMING_SNAKE_CASE    			=       backbone(**_UpperCamelCase	,							output_hidden_states=_UpperCamelCase      )
																	self.assertIsNotNone(outputs.hidden_states      )
																	self.assertTrue(len(outputs.hidden_states      )	,							len(backbone.stage_names      )      )
																	# We skip the stem layer
																	for hidden_states, n_channels in zip(outputs.hidden_states[1:]	,							backbone.channels      ):
																						for hidden_state in hidden_states:
																											# Hidden states are in the format (batch_size, (height * width), n_channels)
																											SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       hidden_state.shape
																											self.assertTrue((h_batch_size, h_n_channels)	,							(batch_size, n_channels)      )

            # Test output_attentions=True
																	if self.has_attentions:
																						SCREAMING_SNAKE_CASE    			=       backbone(**_UpperCamelCase	,							output_attentions=_UpperCamelCase      )
																						self.assertIsNotNone(outputs.attentions      )





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							(KDPMaDiscreteScheduler,)
							lowercase__						:       Optional[int]								=							10




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase      )
												return config




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



												'''simple docstring'''

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




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



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=_UpperCamelCase	,							beta_end=_UpperCamelCase      )




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



												'''simple docstring'''

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




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



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config(prediction_type="v_prediction"      )
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												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() - 0.0_0_0_2      ) < 1e-3




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3





							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if str(_UpperCamelCase      ).startswith("cpu"      ):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




