code: 
  
  
  
  
  
  
  """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							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			List[str]=3		,    a_							:			Optional[Any]=32		,    a_							:			str=3		,    a_							:			int=10		,    a_							:			Optional[Any]=[10, 20, 30, 40]		,    a_							:			Optional[int]=[1, 1, 2, 1]		,    a_							:			List[str]=True		,    a_							:			List[str]=True		,    a_							:			Any="relu"		,    a_							:			str=3		,    a_							:			int=None		,    a_							:			Any=["stage2", "stage3", "stage4"]		,    a_							:			Optional[int]=[2, 3, 4]		,    ):
  				lowerCAmelCase_     :							Any             =    parent
  				lowerCAmelCase_     :							List[Any]             =    batch_size
  				lowerCAmelCase_     :							Any             =    image_size
  				lowerCAmelCase_     :							Dict             =    num_channels
  				lowerCAmelCase_     :							Optional[int]             =    embeddings_size
  				lowerCAmelCase_     :							Tuple             =    hidden_sizes
  				lowerCAmelCase_     :							List[str]             =    depths
  				lowerCAmelCase_     :							Any             =    is_training
  				lowerCAmelCase_     :							str             =    use_labels
  				lowerCAmelCase_     :							Dict             =    hidden_act
  				lowerCAmelCase_     :							List[Any]             =    num_labels
  				lowerCAmelCase_     :							List[Any]             =    scope
  				lowerCAmelCase_     :							Optional[Any]             =    len(a_			)
  				lowerCAmelCase_     :							List[Any]             =    out_features
  				lowerCAmelCase_     :							List[Any]             =    out_indices
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							str             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
  				lowerCAmelCase_     :							Tuple             =    None
  				if self.use_labels:
  						lowerCAmelCase_     :							List[Any]             =    ids_tensor([self.batch_size]		,    self.num_labels			)
  
  				lowerCAmelCase_     :							List[Any]             =    self.get_config()
  
  				return config, pixel_values, labels
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  				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      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			str		,    a_							:			Union[str, Any]		,    a_							:			Any			):
  				lowerCAmelCase_     :							Optional[int]             =    ResNetModel(config=a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							Dict             =    model(a_			)
  				# expected last hidden states: B, C, H // 32, W // 32
  				self.parent.assertEqual(
  				    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str		,    a_							:			str		,    a_							:			Dict		,    a_							:			List[Any]			):
  				lowerCAmelCase_     :							Optional[int]             =    self.num_labels
  				lowerCAmelCase_     :							Any             =    ResNetForImageClassification(a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							List[Any]             =    model(a_		,    labels=a_			)
  				self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			Tuple		,    a_							:			Union[str, Any]		,    a_							:			List[Any]			):
  				lowerCAmelCase_     :							Optional[Any]             =    ResNetBackbone(config=a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							Dict             =    model(a_			)
  
  				# 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
  				lowerCAmelCase_     :							str             =    None
  				lowerCAmelCase_     :							str             =    ResNetBackbone(config=a_			)
  				model.to(a_			)
  				model.eval()
  				lowerCAmelCase_     :							Optional[int]             =    model(a_			)
  
  				# 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      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							int             =    self.prepare_config_and_inputs()
  				lowerCAmelCase_     :							Tuple             =    config_and_inputs
  				lowerCAmelCase_     :							Tuple             =    {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Optional[int]      =							(
  		    (
  		        ResNetModel,
  		        ResNetForImageClassification,
  		        ResNetBackbone,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		a_			:       Optional[Any]      =							(
  		    {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
  		    if is_torch_available()
  		    else {}
  		)
  
  		a_			:       Tuple      =							True
  		a_			:       Tuple      =							False
  		a_			:       Tuple      =							False
  		a_			:       List[Any]      =							False
  		a_			:       Union[str, Any]      =							False
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							List[Any]             =    ResNetModelTester(self			)
  				lowerCAmelCase_     :							Tuple             =    ConfigTester(self		,    config_class=a_		,    has_text_modality=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     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      lowerCamelCase (     self							:			Optional[int]			):
  				return
  
  
  
  
  
  		@unittest.skip(reason="ResNet does not use inputs_embeds"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="ResNet does not support input and output embeddings"			)
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_     :							Optional[int]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							int             =    model_class(a_			)
  						lowerCAmelCase_     :							List[Any]             =    inspect.signature(model.forward			)
  						# signature.parameters is an OrderedDict => so arg_names order is deterministic
  						lowerCAmelCase_     :							Optional[Any]             =    [*signature.parameters.keys()]
  
  						lowerCAmelCase_     :							Dict             =    ["pixel_values"]
  						self.assertListEqual(arg_names[:1]		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_     :							int             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_backbone(*a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_     :							str             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Dict             =    model_class(config=a_			)
  						for name, module in model.named_modules():
  								if isinstance(a_		,    (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      lowerCamelCase (     self							:			str			):
  				def check_hidden_states_output(a_							:			Any		,    a_							:			Any		,    a_							:			Union[str, Any]			):
  						lowerCAmelCase_     :							List[str]             =    model_class(a_			)
  						model.to(a_			)
  						model.eval()
  
  						with torch.no_grad():
  								lowerCAmelCase_     :							Optional[int]             =    model(**self._prepare_for_class(a_		,    a_			)			)
  
  						lowerCAmelCase_     :							Optional[int]             =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  						lowerCAmelCase_     :							Any             =    self.model_tester.num_stages
  						self.assertEqual(len(a_			)		,    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]		,    )
  
  				lowerCAmelCase_     :							int             =    self.model_tester.prepare_config_and_inputs_for_common()
  				lowerCAmelCase_     :							str             =    ["basic", "bottleneck"]
  				for model_class in self.all_model_classes:
  						for layer_type in layers_type:
  								lowerCAmelCase_     :							Optional[Any]             =    layer_type
  								lowerCAmelCase_     :							Optional[int]             =    True
  								check_hidden_states_output(a_		,    a_		,    a_			)
  
  								# check that output_hidden_states also work using config
  								del inputs_dict["output_hidden_states"]
  								lowerCAmelCase_     :							Optional[Any]             =    True
  
  								check_hidden_states_output(a_		,    a_		,    a_			)
  
  
  
  
  
  		@unittest.skip(reason="ResNet does not use feedforward chunking"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							str             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_image_classification(*a_			)
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						lowerCAmelCase_     :							str             =    ResNetModel.from_pretrained(a_			)
  						self.assertIsNotNone(a_			)
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						)		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Optional[Any]             =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
  		return image
  
  
  @require_torch
  @require_vision
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		@cached_property
  		def      lowerCamelCase (     self							:			Any			):
  				return (
  				    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
  				    if is_vision_available()
  				    else None
  				)
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							List[Any]             =    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(a_			)
  
  				lowerCAmelCase_     :							List[str]             =    self.default_image_processor
  				lowerCAmelCase_     :							Union[str, Any]             =    prepare_img()
  				lowerCAmelCase_     :							Optional[int]             =    image_processor(images=a_		,    return_tensors="pt"			).to(a_			)
  
  				# forward pass
  				with torch.no_grad():
  						lowerCAmelCase_     :							Tuple             =    model(**a_			)
  
  				# verify the logits
  				lowerCAmelCase_     :							Dict             =    torch.Size((1, 10_00)			)
  				self.assertEqual(outputs.logits.shape		,    a_			)
  
  				lowerCAmelCase_     :							int             =    torch.tensor([-11.1069, -9.7877, -8.3777]			).to(a_			)
  
  				self.assertTrue(torch.allclose(outputs.logits[0, :3]		,    a_		,    atol=1e-4			)			)
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Union[str, Any]      =							(ResNetBackbone,) if is_torch_available() else ()
  		a_			:       int      =							False
  		a_			:       int      =							ResNetConfig
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_     :							Optional[int]             =    ResNetModelTester(self			)
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  @dataclass
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       torch.FloatTensor
  		a_			:       torch.FloatTensor
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if alpha_transform_type == "cosine":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
  
  		elif alpha_transform_type == "exp":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.exp(t * -12.0 )
  
  		else:
  				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
  
  		lowerCAmelCase_     :							List[str]             =    []
  		for i in range(__UpperCamelCase ):
  				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
  				lowerCAmelCase_     :							Optional[Any]             =    (i + 1) / num_diffusion_timesteps
  				betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )   , __UpperCamelCase ) )
  		return torch.tensor(__UpperCamelCase   , dtype=torch.floataa )
  
  
  
  class 	__lowerCamelCase       (				A__				,							A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Tuple      =							1
  
  
  
  
  
  		@register_to_config
  		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
  				if trained_betas is not None:
  						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
  				elif beta_schedule == "linear":
  						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
  				elif beta_schedule == "scaled_linear":
  						# this schedule is very specific to the latent diffusion model.
  						lowerCAmelCase_     :							Optional[Any]             =    (
  						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
  						)
  				elif beta_schedule == "squaredcos_cap_v2":
  						# Glide cosine schedule
  						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
  				elif beta_schedule == "sigmoid":
  						# GeoDiff sigmoid schedule
  						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
  						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
  				else:
  						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
  				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				# standard deviation of the initial noise distribution
  				lowerCAmelCase_     :							List[str]             =    1.0
  
  				# setable values
  				lowerCAmelCase_     :							Dict             =    None
  				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)
  
  				lowerCAmelCase_     :							int             =    eta
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
  				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
  				lowerCAmelCase_     :							Dict             =    num_inference_steps
  
  				lowerCAmelCase_     :							List[str]             =    []
  
  				lowerCAmelCase_     :							Dict             =    {}
  				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
  						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1
  
  				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
  				while t >= 1:
  						lowerCAmelCase_     :							int             =    t - 1
  						timesteps.append(a_			)
  
  						if jumps.get(a_		,    0			) > 0:
  								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
  								for _ in range(a_			):
  										lowerCAmelCase_     :							Any             =    t + 1
  										timesteps.append(a_			)
  
  				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
  				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
  				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps
  
  				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
  				lowerCAmelCase_     :							str             =    1 - alpha_prod_t_prev
  
  				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
  				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
  				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
  				# variance to pred_sample
  				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
  				# without eta.
  				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
  				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  				return variance
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
  				lowerCAmelCase_     :							Any             =    timestep
  				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps
  
  				# 1. compute alphas, betas
  				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							Optional[Any]             =    1 - alpha_prod_t
  
  				# 2. compute predicted original sample from predicted noise also called
  				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
  				# 3. Clip "predicted x_0"
  				if self.config.clip_sample:
  						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -1		,    1			)
  
  				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
  				# substitute formula (7) in the algorithm coming from DDPM paper
  				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
  				# DDIM schedule gives the same results as DDPM with eta = 1.0
  				# Noise is being reused in 7. and 8., but no impact on quality has
  				# been observed.
  
  				# 5. Add noise
  				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
  				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
  				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5
  
  				lowerCAmelCase_     :							Optional[int]             =    0
  				if t > 0 and self.eta > 0:
  						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise
  
  				# 6. compute "direction pointing to x_t" of formula (12)
  				# from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
  
  				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Dict             =    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
  				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
  				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Tuple             =    mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
  				if not return_dict:
  						return (
  						    pred_prev_sample,
  						    pred_original_sample,
  						)
  
  				return RePaintSchedulerOutput(prev_sample=a_		,    pred_original_sample=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
  				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps
  
  				for i in range(a_			):
  						lowerCAmelCase_     :							Optional[Any]             =    self.betas[timestep + i]
  						if sample.device.type == "mps":
  								# randn does not work reproducibly on mps
  								lowerCAmelCase_     :							str             =    randn_tensor(sample.shape		,    dtype=sample.dtype		,    generator=a_			)
  								lowerCAmelCase_     :							List[str]             =    noise.to(sample.device			)
  						else:
  								lowerCAmelCase_     :							Tuple             =    randn_tensor(sample.shape		,    generator=a_		,    device=sample.device		,    dtype=sample.dtype			)
  
  						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
  						lowerCAmelCase_     :							int             =    (1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
  				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)
  
  
  
  
  
  		def __len__(     self							:			Union[str, Any]			):
  				return self.config.num_train_timesteps
  