





"""simple docstring"""




import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
						import torch
						from torch import nn

						from transformers import DinatBackbone, DinatForImageClassification, DinatModel
						from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor




class 					_a		:






				def __init__(					self						:       Dict,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Any=1_3,					lowerCAmelCase__						:       str=6_4,					lowerCAmelCase__						:       Optional[int]=4,					lowerCAmelCase__						:       str=3,					lowerCAmelCase__						:       Optional[Any]=1_6,					lowerCAmelCase__						:       Tuple=[1, 2, 1],					lowerCAmelCase__						:       int=[2, 4, 8],					lowerCAmelCase__						:       Optional[Any]=3,					lowerCAmelCase__						:       str=[[3], [1, 2], [1]],					lowerCAmelCase__						:       Dict=2.0,					lowerCAmelCase__						:       Tuple=True,					lowerCAmelCase__						:       List[Any]=0.0,					lowerCAmelCase__						:       Tuple=0.0,					lowerCAmelCase__						:       Tuple=0.1,					lowerCAmelCase__						:       List[str]="gelu",					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Dict=0.02,					lowerCAmelCase__						:       Optional[Any]=1e-5,					lowerCAmelCase__						:       List[str]=True,					lowerCAmelCase__						:       int=None,					lowerCAmelCase__						:       Any=True,					lowerCAmelCase__						:       Optional[Any]=1_0,					lowerCAmelCase__						:       Any=["stage1", "stage2"],					lowerCAmelCase__						:       Union[str, Any]=[1, 2],					)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       parent
										_UpperCamelCase     :   Optional[int]      =       batch_size
										_UpperCamelCase     :   int      =       image_size
										_UpperCamelCase     :   List[str]      =       patch_size
										_UpperCamelCase     :   Any      =       num_channels
										_UpperCamelCase     :   List[Any]      =       embed_dim
										_UpperCamelCase     :   int      =       depths
										_UpperCamelCase     :   Optional[Any]      =       num_heads
										_UpperCamelCase     :   str      =       kernel_size
										_UpperCamelCase     :   Union[str, Any]      =       dilations
										_UpperCamelCase     :   List[Any]      =       mlp_ratio
										_UpperCamelCase     :   List[str]      =       qkv_bias
										_UpperCamelCase     :   List[Any]      =       hidden_dropout_prob
										_UpperCamelCase     :   List[str]      =       attention_probs_dropout_prob
										_UpperCamelCase     :   Dict      =       drop_path_rate
										_UpperCamelCase     :   int      =       hidden_act
										_UpperCamelCase     :   List[Any]      =       patch_norm
										_UpperCamelCase     :   List[str]      =       layer_norm_eps
										_UpperCamelCase     :   Tuple      =       initializer_range
										_UpperCamelCase     :   Any      =       is_training
										_UpperCamelCase     :   List[Any]      =       scope
										_UpperCamelCase     :   Any      =       use_labels
										_UpperCamelCase     :   Optional[int]      =       num_labels
										_UpperCamelCase     :   List[str]      =       out_features
										_UpperCamelCase     :   str      =       out_indices






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




										'''simple docstring'''



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

										_UpperCamelCase     :   Dict      =       None
										if self.use_labels:
																_UpperCamelCase     :   int      =       ids_tensor([self.batch_size],					self.num_labels				)

										_UpperCamelCase     :   str      =       self.get_config()

										return config, pixel_values, labels






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




										'''simple docstring'''



										return DinatConfig(
										    num_labels=self.num_labels,					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,					kernel_size=self.kernel_size,					dilations=self.dilations,					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,					patch_norm=self.patch_norm,					layer_norm_eps=self.layer_norm_eps,					initializer_range=self.initializer_range,					out_features=self.out_features,					out_indices=self.out_indices,					)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       DinatModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       (config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
										_UpperCamelCase     :   str      =       int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

										self.parent.assertEqual(
										    result.last_hidden_state.shape,					(self.batch_size, expected_height, expected_width, expected_dim)				)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       DinatForImageClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__,					labels=lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)

										# test greyscale images
										_UpperCamelCase     :   Any      =       1
										_UpperCamelCase     :   List[str]      =       DinatForImageClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										_UpperCamelCase     :   str      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
										_UpperCamelCase     :   Optional[int]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       DinatBackbone(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__				)

										# verify hidden states
										self.parent.assertEqual(len(result.feature_maps				),					len(config.out_features				)				)
										self.parent.assertListEqual(list(result.feature_maps[0].shape				),					[self.batch_size, model.channels[0], 1_6, 1_6]				)

										# verify channels
										self.parent.assertEqual(len(model.channels				),					len(config.out_features				)				)

										# verify backbone works with out_features=None
										_UpperCamelCase     :   List[Any]      =       None
										_UpperCamelCase     :   Union[str, Any]      =       DinatBackbone(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__				)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps				),					1				)
										self.parent.assertListEqual(list(result.feature_maps[0].shape				),					[self.batch_size, model.channels[-1], 4, 4]				)

										# verify channels
										self.parent.assertEqual(len(model.channels				),					1				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.prepare_config_and_inputs()
										_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       config_and_inputs
										_UpperCamelCase     :   str      =       {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_natten
@require_torch
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (
				    (
				        DinatModel,
				        DinatForImageClassification,
				        DinatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase								=       (
				    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase								=       False

				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False






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




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       DinatModelTester(self				)
										_UpperCamelCase     :   Optional[Any]      =       ConfigTester(self,					config_class=lowerCAmelCase__,					embed_dim=3_7				)






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




										'''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						:       Optional[int]				)      -> int:




										'''simple docstring'''



										return






				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase__				)






				@unittest.skip(reason='''Dinat does not use inputs_embeds'''				)
				def 							snake_case      (					self						:       str				)      -> int:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''Dinat does not use feedforward chunking'''				)
				def 							snake_case      (					self						:       Tuple				)      -> Optional[int]:




										'''simple docstring'''



										pass






				def 							snake_case      (					self						:       Any				)      -> str:




										'''simple docstring'''



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

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






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




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase     :   str      =       ['''pixel_values''']
																self.assertListEqual(arg_names[:1],					lowerCAmelCase__				)






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




										'''simple docstring'''



										self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       model_class(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										with torch.no_grad():
																_UpperCamelCase     :   Optional[int]      =       model(**self._prepare_for_class(lowerCAmelCase__,					lowerCAmelCase__				)				)

										_UpperCamelCase     :   Dict      =       outputs.hidden_states

										_UpperCamelCase     :   List[str]      =       getattr(
										    self.model_tester,					'''expected_num_hidden_layers''',					len(self.model_tester.depths				) + 1				)
										self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

										# Dinat has a different seq_length
										_UpperCamelCase     :   Dict      =       (
										    config.patch_size
										    if isinstance(config.patch_size,					collections.abc.Iterable				)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCamelCase     :   Any      =       image_size[0] // patch_size[0]
										_UpperCamelCase     :   Optional[Any]      =       image_size[1] // patch_size[1]

										self.assertListEqual(
										    list(hidden_states[0].shape[-3:]				),					[height, width, self.model_tester.embed_dim],					)

										if model_class.__name__ != "DinatBackbone":
																_UpperCamelCase     :   Optional[int]      =       outputs.reshaped_hidden_states
																self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

																_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       reshaped_hidden_states[0].shape
																_UpperCamelCase     :   int      =       (
																    reshaped_hidden_states[0].view(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				).permute(0,					2,					3,					1				)
																)
																self.assertListEqual(
																    list(reshaped_hidden_states.shape[-3:]				),					[height, width, self.model_tester.embed_dim],					)






				def 							snake_case      (					self						:       Tuple				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCamelCase     :   Optional[Any]      =       (
										    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:
																_UpperCamelCase     :   List[Any]      =       True
																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

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

																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






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




										'''simple docstring'''



										for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase     :   Tuple      =       DinatModel.from_pretrained(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Any      =       _config_zero_init(lowerCAmelCase__				)
										for model_class in self.all_model_classes:
																_UpperCamelCase     :   str      =       model_class(config=lowerCAmelCase__				)
																for name, param in model.named_parameters():
																						if "embeddings" not in name and param.requires_grad:
																												self.assertIn(
																												    ((param.data.mean() * 1e9).round() / 1e9).item(),					[0.0, 1.0],					msg=f"""Parameter {name} of model {model_class} seems not properly initialized""",					)




@require_natten
@require_vision
@require_torch
class 					_a		( unittest.TestCase							):






				@cached_property
				def 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



										return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''				) if is_vision_available() else None






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   List[Any]      =       self.default_image_processor

										_UpperCamelCase     :   Union[str, Any]      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
										_UpperCamelCase     :   Any      =       image_processor(images=lowerCAmelCase__,					return_tensors='''pt'''				).to(lowerCAmelCase__				)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase     :   str      =       model(**lowerCAmelCase__				)

										# verify the logits
										_UpperCamelCase     :   Any      =       torch.Size((1, 1_0_0_0)				)
										self.assertEqual(outputs.logits.shape,					lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       torch.tensor([-0.1_545, -0.7_667, 0.4_642]				).to(lowerCAmelCase__				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3],					lowerCAmelCase__,					atol=1e-4				)				)




@require_torch
@require_natten
class 					_a		( unittest.TestCase      ,							_lowerCAmelCase							):
				UpperCamelCase								=       (DinatBackbone,) if is_torch_available() else ()
				UpperCamelCase								=       DinatConfig






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




										'''simple docstring'''



										_UpperCamelCase     :   str      =       DinatModelTester(self				)




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase      :				Dict     			=logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							None







def     A__	(       __A ,			__A=0.999 ,			__A="cosine" ,			):







    '''simple docstring'''
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__A						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__A						):
            return math.exp(t * -12.0						)

    else:
        raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""						)

    _lowerCamelCase  :		int			    =    []
    for i in range(__A						):
        _lowerCamelCase  :		Optional[int]			    =    i / num_diffusion_timesteps
        _lowerCamelCase  :		int			    =    (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__A						) / alpha_bar_fn(__A						) ,			__A						)						)
    return torch.tensor(__A ,			dtype=torch.floataa						)




class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							[e.name for e in KarrasDiffusionSchedulers]
      _snake_case     		=							1



      @register_to_config
      def __init__(       self  :					Tuple     ,	_UpperCamelCase  :					int = 1000     ,	_UpperCamelCase  :					float = 0.0_0_0_1     ,	_UpperCamelCase  :					float = 0.0_2     ,	_UpperCamelCase  :					str = "linear"     ,	_UpperCamelCase  :					Optional[Union[np.ndarray, List[float]]] = None     ,	_UpperCamelCase  :					str = "epsilon"     ,	_UpperCamelCase  :					str = "linspace"     ,	_UpperCamelCase  :					int = 0     ,	)					->Optional[int]:


          """simple docstring"""


          if trained_betas is not None:
              _lowerCamelCase  :		List[str]			    =    torch.tensor(_UpperCamelCase     ,	dtype=torch.floataa)
          elif beta_schedule == "linear":
              _lowerCamelCase  :		List[str]			    =    torch.linspace(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	dtype=torch.floataa)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _lowerCamelCase  :		int			    =    (
                  torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	_UpperCamelCase     ,	dtype=torch.floataa) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _lowerCamelCase  :		Any			    =    betas_for_alpha_bar(_UpperCamelCase)
          else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

          _lowerCamelCase  :		Tuple			    =    1.0 - self.betas
          _lowerCamelCase  :		int			    =    torch.cumprod(self.alphas     ,	dim=0)

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Dict			    =    np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Tuple			    =    torch.from_numpy(_UpperCamelCase)

          # setable values
          _lowerCamelCase  :		List[str]			    =    None
          _lowerCamelCase  :		int			    =    np.linspace(0     ,	num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[::-1].copy()
          _lowerCamelCase  :		Optional[Any]			    =    torch.from_numpy(_UpperCamelCase)
          _lowerCamelCase  :		Tuple			    =    False



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Dict:


          """simple docstring"""


          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor])					->torch.FloatTensor:


          """simple docstring"""


          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[int]			    =    timestep.to(self.timesteps.device)
          _lowerCamelCase  :		Tuple			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		str			    =    self.sigmas[step_index]
          _lowerCamelCase  :		List[str]			    =    sample / ((sigma**2 + 1) ** 0.5)
          _lowerCamelCase  :		str			    =    True
          return sample



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Union[str, torch.device] = None)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              _lowerCamelCase  :		Any			    =    np.linspace(0     ,	self.config.num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              _lowerCamelCase  :		Tuple			    =    self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _lowerCamelCase  :		List[Any]			    =    (np.arange(0     ,	_UpperCamelCase) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              _lowerCamelCase  :		List[str]			    =    self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _lowerCamelCase  :		str			    =    (np.arange(self.config.num_train_timesteps     ,	0     ,	-step_ratio)).round().copy().astype(_UpperCamelCase)
              timesteps -= 1
          else:
              raise ValueError(
                  F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Union[str, Any]			    =    np.interp(_UpperCamelCase     ,	np.arange(0     ,	len(_UpperCamelCase))     ,	_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    np.concatenate([sigmas, [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Dict			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)
          if str(_UpperCamelCase).startswith("""mps"""):
              # mps does not support float64
              _lowerCamelCase  :		List[str]			    =    torch.from_numpy(_UpperCamelCase).to(_UpperCamelCase     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Any			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Optional[torch.Generator] = None     ,	_UpperCamelCase  :					bool = True     ,	)					->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


          """simple docstring"""


          if (
              isinstance(_UpperCamelCase     ,	_UpperCamelCase)
              or isinstance(_UpperCamelCase     ,	torch.IntTensor)
              or isinstance(_UpperCamelCase     ,	torch.LongTensor)
          ):
              raise ValueError(
                  (
                      """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                      """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                      """ one of the `scheduler.timesteps` as a timestep."""
                  )     ,	)

          if not self.is_scale_input_called:
              logger.warning(
                  """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                  """See `StableDiffusionPipeline` for a usage example.""")

          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[Any]			    =    timestep.to(self.timesteps.device)

          _lowerCamelCase  :		Optional[int]			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		Union[str, Any]			    =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              _lowerCamelCase  :		int			    =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              _lowerCamelCase  :		Optional[Any]			    =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError("""prediction_type not implemented yet: sample""")
          else:
              raise ValueError(
                  F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

          _lowerCamelCase  :		Tuple			    =    self.sigmas[step_index]
          _lowerCamelCase  :		int			    =    self.sigmas[step_index + 1]
          _lowerCamelCase  :		Tuple			    =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          _lowerCamelCase  :		List[Any]			    =    (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          _lowerCamelCase  :		List[Any]			    =    (sample - pred_original_sample) / sigma

          _lowerCamelCase  :		Tuple			    =    sigma_down - sigma

          _lowerCamelCase  :		Optional[int]			    =    sample + derivative * dt

          _lowerCamelCase  :		int			    =    model_output.device
          _lowerCamelCase  :		List[Any]			    =    randn_tensor(model_output.shape     ,	dtype=model_output.dtype     ,	device=_UpperCamelCase     ,	generator=_UpperCamelCase)

          _lowerCamelCase  :		List[Any]			    =    prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=_UpperCamelCase     ,	pred_original_sample=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	)					->torch.FloatTensor:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.sigmas.to(device=original_samples.device     ,	dtype=original_samples.dtype)
          if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase):
              # mps does not support float64
              _lowerCamelCase  :		str			    =    self.timesteps.to(original_samples.device     ,	dtype=torch.floataa)
              _lowerCamelCase  :		Union[str, Any]			    =    timesteps.to(original_samples.device     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Union[str, Any]			    =    self.timesteps.to(original_samples.device)
              _lowerCamelCase  :		List[str]			    =    timesteps.to(original_samples.device)

          _lowerCamelCase  :		Optional[int]			    =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          _lowerCamelCase  :		List[Any]			    =    sigmas[step_indices].flatten()
          while len(sigma.shape) < len(original_samples.shape):
              _lowerCamelCase  :		Optional[Any]			    =    sigma.unsqueeze(-1)

          _lowerCamelCase  :		List[str]			    =    original_samples + noise * sigma
          return noisy_samples




      def __len__(       self  :					Dict)					->str:


          """simple docstring"""


          return self.config.num_train_timesteps




