
'''simple docstring'''
import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
				from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor


class     _lowerCAmelCase					:







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase=3						,						UpperCAmelCase=32						,						UpperCAmelCase=3						,						UpperCAmelCase=10						,						UpperCAmelCase=[10, 20, 30, 40]						,						UpperCAmelCase=[1, 1, 2, 1]						,						UpperCAmelCase=True						,						UpperCAmelCase=True						,						UpperCAmelCase="relu"						,						UpperCAmelCase=3						,						UpperCAmelCase=None						,						UpperCAmelCase=["stage2", "stage3", "stage4"]						,						UpperCAmelCase=[2, 3, 4]						,						)  ->     Optional[int]:
														_snake_case     =						parent
														_snake_case     =						batch_size
														_snake_case     =						image_size
														_snake_case     =						num_channels
														_snake_case     =						embeddings_size
														_snake_case     =						hidden_sizes
														_snake_case     =						depths
														_snake_case     =						is_training
														_snake_case     =						use_labels
														_snake_case     =						hidden_act
														_snake_case     =						num_labels
														_snake_case     =						scope
														_snake_case     =						len(UpperCAmelCase						)
														_snake_case     =						out_features
														_snake_case     =						out_indices

							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

														_snake_case     =						self.get_config()

														return config, pixel_values, labels

							def  lowercase							(self						)  ->     List[Any]:
														return ResNetConfig(
														    num_channels=self.num_channels						,						embeddings_size=self.embeddings_size						,						hidden_sizes=self.hidden_sizes						,						depths=self.depths						,						hidden_act=self.hidden_act						,						num_labels=self.num_labels						,						out_features=self.out_features						,						out_indices=self.out_indices						,						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     List[Any]:
														_snake_case     =						ResNetModel(config=UpperCAmelCase						)
														model.to(UpperCAmelCase						)
														model.eval()
														_snake_case     =						model(UpperCAmelCase						)
														# expected last hidden states: B, C, H // 32, W // 32
														self.parent.assertEqual(
														    result.last_hidden_state.shape						,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Any:
														_snake_case     =						self.num_labels
														_snake_case     =						ResNetForImageClassification(UpperCAmelCase						)
														model.to(UpperCAmelCase						)
														model.eval()
														_snake_case     =						model(UpperCAmelCase						,						labels=UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Tuple:
														_snake_case     =						ResNetBackbone(config=UpperCAmelCase						)
														model.to(UpperCAmelCase						)
														model.eval()
														_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						)						,						[self.batch_size, self.hidden_sizes[1], 4, 4]						)

														# verify channels
														self.parent.assertEqual(len(model.channels						)						,						len(config.out_features						)						)
														self.parent.assertListEqual(model.channels						,						config.hidden_sizes[1:]						)

														# verify backbone works with out_features=None
														_snake_case     =						None
														_snake_case     =						ResNetBackbone(config=UpperCAmelCase						)
														model.to(UpperCAmelCase						)
														model.eval()
														_snake_case     =						model(UpperCAmelCase						)

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

														# verify channels
														self.parent.assertEqual(len(model.channels						)						,						1						)
														self.parent.assertListEqual(model.channels						,						[config.hidden_sizes[-1]]						)





							def  lowercase							(self						)  ->     List[Any]:
														_snake_case     =						self.prepare_config_and_inputs()
														_snake_case,							_snake_case,							_snake_case     =						config_and_inputs
														_snake_case     =						{"""pixel_values""": pixel_values}
														return config, inputs_dict


@require_torch
class     _lowerCAmelCase					(					__snake_case  ,							__snake_case  ,							unittest.TestCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		(
							    (
							        ResNetModel,
							        ResNetForImageClassification,
							        ResNetBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_      		=		(
							    {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase_      		=		True
							lowerCAmelCase_      		=		False
							lowerCAmelCase_      		=		False
							lowerCAmelCase_      		=		False
							lowerCAmelCase_      		=		False

							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						ResNetModelTester(self						)
														_snake_case     =						ConfigTester(self						,						config_class=UpperCAmelCase						,						has_text_modality=UpperCAmelCase						)

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

							@unittest.skip(reason="""ResNet does not use inputs_embeds"""						)
							def  lowercase							(self						)  ->     Dict:
														pass

							@unittest.skip(reason="""ResNet does not support input and output embeddings"""						)
							def  lowercase							(self						)  ->     Any:
														pass

							def  lowercase							(self						)  ->     Tuple:
														_snake_case,							_snake_case     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					_snake_case     =						["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]						,						UpperCAmelCase						)

							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase						)

							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_backbone(*UpperCAmelCase						)

							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case,							_snake_case     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_snake_case     =						model_class(config=UpperCAmelCase						)
																					for name, module in model.named_modules():
																												if isinstance(UpperCAmelCase						,						(nn.BatchNormad, nn.GroupNorm)						):
																																			self.assertTrue(
																																			    torch.all(module.weight == 1						)						,						msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""						,						)
																																			self.assertTrue(
																																			    torch.all(module.bias == 0						)						,						msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""						,						)

							def  lowercase							(self						)  ->     List[str]:
														def check_hidden_states_output(UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						):
																					_snake_case     =						model_class(UpperCAmelCase						)
																					model.to(UpperCAmelCase						)
																					model.eval()

																					with torch.no_grad():
																												_snake_case     =						model(**self._prepare_for_class(UpperCAmelCase						,						UpperCAmelCase						)						)

																					_snake_case     =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					_snake_case     =						self.model_tester.num_stages
																					self.assertEqual(len(UpperCAmelCase						)						,						expected_num_stages + 1						)

																					# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]						)						,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,						)

														_snake_case,							_snake_case     =						self.model_tester.prepare_config_and_inputs_for_common()
														_snake_case     =						["""basic""", """bottleneck"""]
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												_snake_case     =						layer_type
																												_snake_case     =						True
																												check_hidden_states_output(UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)

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

																												check_hidden_states_output(UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)

							@unittest.skip(reason="""ResNet does not use feedforward chunking"""						)
							def  lowercase							(self						)  ->     List[str]:
														pass

							def  lowercase							(self						)  ->     Any:
														_snake_case     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase						)





							@slow
							def  lowercase							(self						)  ->     List[Any]:
														for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_snake_case     =						ResNetModel.from_pretrained(UpperCAmelCase						)
																					self.assertIsNotNone(UpperCAmelCase						)






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image


@require_torch
@require_vision
class     _lowerCAmelCase					(					unittest.TestCase   ):







							'''simple docstring'''

							@cached_property
							def  lowercase							(self						)  ->     Optional[int]:
														return (
														    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
														    if is_vision_available()
														    else None
														)





							@slow
							def  lowercase							(self						)  ->     Dict:
														_snake_case     =						ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(UpperCAmelCase						)

														_snake_case     =						self.default_image_processor
														_snake_case     =						prepare_img()
														_snake_case     =						image_processor(images=UpperCAmelCase						,						return_tensors="""pt"""						).to(UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					_snake_case     =						model(**UpperCAmelCase						)

														# verify the logits
														_snake_case     =						torch.Size((1, 1000)						)
														self.assertEqual(outputs.logits.shape						,						UpperCAmelCase						)

														_snake_case     =						torch.tensor([-11.1069, -9.7877, -8.3777]						).to(UpperCAmelCase						)

														self.assertTrue(torch.allclose(outputs.logits[0, :3]						,						UpperCAmelCase						,						atol=1e-4						)						)


@require_torch
class     _lowerCAmelCase					(					__snake_case  ,							unittest.TestCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		(ResNetBackbone,) if is_torch_available() else ()
							lowerCAmelCase_      		=		False
							lowerCAmelCase_      		=		ResNetConfig

							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						ResNetModelTester(self						)

'''simple docstring'''
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  							= logging.get_logger(__name__)  # pylint: disable=invalid-name


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







							'''simple docstring'''

							lowerCAmelCase_      		=		42
							lowerCAmelCase_      		=		None






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=0.999							,      _SCREAMING_SNAKE_CASE="cosine"							,      ):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

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


class     _lowerCAmelCase					(					__snake_case  ,							__snake_case   ):







							'''simple docstring'''

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

							@register_to_config
							def __init__(self						,						UpperCAmelCase = 1000						,						UpperCAmelCase = 0.0001						,						UpperCAmelCase = 0.02						,						UpperCAmelCase = "linear"						,						UpperCAmelCase = None						,						UpperCAmelCase = "epsilon"						,						UpperCAmelCase = "linear"						,						UpperCAmelCase = False						,						UpperCAmelCase = "linspace"						,						UpperCAmelCase = 0						,						)  ->     Tuple:
														if trained_betas is not None:
																					_snake_case     =						torch.tensor(UpperCAmelCase						,						dtype=torch.floataa						)
														elif beta_schedule == "linear":
																					_snake_case     =						torch.linspace(UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						dtype=torch.floataa						)
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					_snake_case     =						(
																					    torch.linspace(beta_start**0.5						,						beta_end**0.5						,						UpperCAmelCase						,						dtype=torch.floataa						) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					_snake_case     =						betas_for_alpha_bar(UpperCAmelCase						)
														else:
																					raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

														_snake_case     =						1.0 - self.betas
														_snake_case     =						torch.cumprod(self.alphas						,						dim=0						)

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

														# setable values
														_snake_case     =						None
														_snake_case     =						np.linspace(0						,						num_train_timesteps - 1						,						UpperCAmelCase						,						dtype=UpperCAmelCase						)[::-1].copy()
														_snake_case     =						torch.from_numpy(UpperCAmelCase						)
														_snake_case     =						False
														_snake_case     =						use_karras_sigmas

							@property
							def  lowercase							(self						)  ->     str:
														# standard deviation of the initial noise distribution
														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

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

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     torch.FloatTensor:
														if isinstance(UpperCAmelCase						,						torch.Tensor						):
																					_snake_case     =						timestep.to(self.timesteps.device						)
														_snake_case     =						(self.timesteps == timestep).nonzero().item()
														_snake_case     =						self.sigmas[step_index]

														_snake_case     =						sample / ((sigma**2 + 1) ** 0.5)

														_snake_case     =						True
														return sample

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Any:
														_snake_case     =						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":
																					_snake_case     =						np.linspace(0						,						self.config.num_train_timesteps - 1						,						UpperCAmelCase						,						dtype=UpperCAmelCase						)[
																					    ::-1
																					].copy()
														elif self.config.timestep_spacing == "leading":
																					_snake_case     =						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
																					_snake_case     =						(np.arange(0						,						UpperCAmelCase						) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase						)
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					_snake_case     =						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
																					_snake_case     =						(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'."""						)

														_snake_case     =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
														_snake_case     =						np.log(UpperCAmelCase						)

														if self.config.interpolation_type == "linear":
																					_snake_case     =						np.interp(UpperCAmelCase						,						np.arange(0						,						len(UpperCAmelCase						)						)						,						UpperCAmelCase						)
														elif self.config.interpolation_type == "log_linear":
																					_snake_case     =						torch.linspace(np.log(sigmas[-1]						)						,						np.log(sigmas[0]						)						,						num_inference_steps + 1						).exp()
														else:
																					raise ValueError(
																					    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
																					    """ 'linear' or 'log_linear'"""						)

														if self.use_karras_sigmas:
																					_snake_case     =						self._convert_to_karras(in_sigmas=UpperCAmelCase						,						num_inference_steps=self.num_inference_steps						)
																					_snake_case     =						np.array([self._sigma_to_t(UpperCAmelCase						,						UpperCAmelCase						) for sigma in sigmas]						)

														_snake_case     =						np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
														_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(device=UpperCAmelCase						)
														if str(UpperCAmelCase						).startswith("""mps"""						):
																					# mps does not support float64
																					_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(UpperCAmelCase						,						dtype=torch.floataa						)
														else:
																					_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(device=UpperCAmelCase						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     str:
														# get log sigma
														_snake_case     =						np.log(UpperCAmelCase						)

														# get distribution
														_snake_case     =						log_sigma - log_sigmas[:, np.newaxis]

														# get sigmas range
														_snake_case     =						np.cumsum((dists >= 0)						,						axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
														_snake_case     =						low_idx + 1

														_snake_case     =						log_sigmas[low_idx]
														_snake_case     =						log_sigmas[high_idx]

														# interpolate sigmas
														_snake_case     =						(low - log_sigma) / (low - high)
														_snake_case     =						np.clip(UpperCAmelCase						,						0						,						1						)

														# transform interpolation to time range
														_snake_case     =						(1 - w) * low_idx + w * high_idx
														_snake_case     =						t.reshape(sigma.shape						)
														return t

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     torch.FloatTensor:
														_snake_case     =						in_sigmas[-1].item()
														_snake_case     =						in_sigmas[0].item()

														_snake_case     =						7.0  # 7.0 is the value used in the paper
														_snake_case     =						np.linspace(0						,						1						,						UpperCAmelCase						)
														_snake_case     =						sigma_min ** (1 / rho)
														_snake_case     =						sigma_max ** (1 / rho)
														_snake_case     =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
														return sigmas

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase = 0.0						,						UpperCAmelCase = 0.0						,						UpperCAmelCase = float("""inf"""						)						,						UpperCAmelCase = 1.0						,						UpperCAmelCase = None						,						UpperCAmelCase = True						,						)  ->     Union[EulerDiscreteSchedulerOutput, Tuple]:

														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						):
																					_snake_case     =						timestep.to(self.timesteps.device						)

														_snake_case     =						(self.timesteps == timestep).nonzero().item()
														_snake_case     =						self.sigmas[step_index]

														_snake_case     =						min(s_churn / (len(self.sigmas						) - 1)						,						2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

														_snake_case     =						randn_tensor(
														    model_output.shape						,						dtype=model_output.dtype						,						device=model_output.device						,						generator=UpperCAmelCase						)

														_snake_case     =						noise * s_noise
														_snake_case     =						sigma * (gamma + 1)

														if gamma > 0:
																					_snake_case     =						sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														# NOTE: "original_sample" should not be an expected prediction_type but is left in for
														# backwards compatibility
														if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																					_snake_case     =						model_output
														elif self.config.prediction_type == "epsilon":
																					_snake_case     =						sample - sigma_hat * model_output
														elif self.config.prediction_type == "v_prediction":
																					# * c_out + input * c_skip
																					_snake_case     =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
														else:
																					raise ValueError(
																					    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

														# 2. Convert to an ODE derivative
														_snake_case     =						(sample - pred_original_sample) / sigma_hat

														_snake_case     =						self.sigmas[step_index + 1] - sigma_hat

														_snake_case     =						sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase						,						pred_original_sample=UpperCAmelCase						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						)  ->     torch.FloatTensor:
														# Make sure sigmas and timesteps have the same device and dtype as original_samples
														_snake_case     =						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
																					_snake_case     =						self.timesteps.to(original_samples.device						,						dtype=torch.floataa						)
																					_snake_case     =						timesteps.to(original_samples.device						,						dtype=torch.floataa						)
														else:
																					_snake_case     =						self.timesteps.to(original_samples.device						)
																					_snake_case     =						timesteps.to(original_samples.device						)

														_snake_case     =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

														_snake_case     =						sigmas[step_indices].flatten()
														while len(sigma.shape						) < len(original_samples.shape						):
																					_snake_case     =						sigma.unsqueeze(-1						)

														_snake_case     =						original_samples + noise * sigma
														return noisy_samples





							def __len__(self						)  ->     Optional[int]:
														return self.config.num_train_timesteps