






"""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			)










"""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
