code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import inspect
  import unittest
  
  import requests
  
  from transformers import MgpstrConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  					import torch
  					from torch import nn
  
  					from transformers import MgpstrForSceneTextRecognition
  
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import MgpstrProcessor
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			str		,    a_							:			str		,    a_							:			Optional[int]=False		,    a_							:			Tuple=13		,    a_							:			Union[str, Any]=(32, 1_28)		,    a_							:			List[Any]=4		,    a_							:			Union[str, Any]=3		,    a_							:			Any=27		,    a_							:			Any=38		,    a_							:			Union[str, Any]=99		,    a_							:			Dict=99		,    a_							:			Tuple=32		,    a_							:			str=5		,    a_							:			List[str]=4		,    a_							:			Optional[Any]=4.0		,    a_							:			List[str]=2_57		,    a_							:			Dict=None		,    ):
  				lowerCAmelCase_     :							str             =    parent
  				lowerCAmelCase_     :							Optional[int]             =    is_training
  				lowerCAmelCase_     :							Dict             =    batch_size
  				lowerCAmelCase_     :							Optional[int]             =    image_size
  				lowerCAmelCase_     :							Dict             =    patch_size
  				lowerCAmelCase_     :							List[Any]             =    num_channels
  				lowerCAmelCase_     :							Optional[int]             =    max_token_length
  				lowerCAmelCase_     :							List[str]             =    num_character_labels
  				lowerCAmelCase_     :							Optional[Any]             =    num_bpe_labels
  				lowerCAmelCase_     :							Optional[Any]             =    num_wordpiece_labels
  				lowerCAmelCase_     :							int             =    hidden_size
  				lowerCAmelCase_     :							Dict             =    num_hidden_layers
  				lowerCAmelCase_     :							Dict             =    num_attention_heads
  				lowerCAmelCase_     :							Any             =    mlp_ratio
  				lowerCAmelCase_     :							List[Any]             =    patch_embeds_hidden_size
  				lowerCAmelCase_     :							Dict             =    output_hidden_states
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_     :							List[Any]             =    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
  				lowerCAmelCase_     :							int             =    self.get_config()
  				return config, pixel_values
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  				return MgpstrConfig(
  				    image_size=self.image_size		,    patch_size=self.patch_size		,    num_channels=self.num_channels		,    max_token_length=self.max_token_length		,    num_character_labels=self.num_character_labels		,    num_bpe_labels=self.num_bpe_labels		,    num_wordpiece_labels=self.num_wordpiece_labels		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    mlp_ratio=self.mlp_ratio		,    output_hidden_states=self.output_hidden_states		,    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Union[str, Any]		,    a_							:			List[str]			):
  				lowerCAmelCase_     :							Union[str, Any]             =    MgpstrForSceneTextRecognition(a_			)
  				model.to(a_			)
  				model.eval()
  				with torch.no_grad():
  						lowerCAmelCase_     :							Any             =    model(a_			)
  				self.parent.assertEqual(
  				    generated_ids[0][0].shape		,    (self.batch_size, self.max_token_length, self.num_character_labels)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_     :							Optional[Any]             =    self.prepare_config_and_inputs()
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Any             =    config_and_inputs
  				lowerCAmelCase_     :							Any             =    {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       List[Any]      =							(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
  		a_			:       List[Any]      =							{"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
  		a_			:       List[str]      =							False
  
  		a_			:       int      =							False
  		a_			:       Optional[Any]      =							False
  		a_			:       int      =							False
  		a_			:       Tuple      =							False
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  				lowerCAmelCase_     :							Dict             =    MgpstrModelTester(self			)
  				lowerCAmelCase_     :							Optional[int]             =    ConfigTester(self		,    config_class=a_		,    has_text_modality=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  				self.config_tester.run_common_tests()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_     :							Tuple             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  		@unittest.skip(reason="MgpstrModel does not use inputs_embeds"			)
  		def      lowerCamelCase (     self							:			List[Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Tuple             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Dict             =    model_class(a_			)
  						self.assertIsInstance(model.get_input_embeddings()		,    (nn.Module)			)
  						lowerCAmelCase_     :							str             =    model.get_output_embeddings()
  						self.assertTrue(x is None or isinstance(a_		,    nn.Linear			)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							List[Any]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Dict             =    model_class(a_			)
  						lowerCAmelCase_     :							Tuple             =    inspect.signature(model.forward			)
  						# signature.parameters is an OrderedDict => so arg_names order is deterministic
  						lowerCAmelCase_     :							Dict             =    [*signature.parameters.keys()]
  
  						lowerCAmelCase_     :							Dict             =    ["pixel_values"]
  						self.assertListEqual(arg_names[:1]		,    a_			)
  
  
  
  
  
  		@unittest.skip(reason="MgpstrModel does not support feedforward chunking"			)
  		def      lowerCamelCase (     self							:			int			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							str             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						if not model_class.supports_gradient_checkpointing:
  								continue
  
  						lowerCAmelCase_     :							Any             =    True
  						lowerCAmelCase_     :							str             =    model_class(a_			)
  						self.assertTrue(model.is_gradient_checkpointing			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  
  
  
  
  
  
  
  				def check_hidden_states_output(a_							:			List[Any]		,    a_							:			str		,    a_							:			Optional[Any]			):
  						lowerCAmelCase_     :							str             =    model_class(a_			)
  						model.to(a_			)
  						model.eval()
  
  						with torch.no_grad():
  								lowerCAmelCase_     :							Union[str, Any]             =    model(**self._prepare_for_class(a_		,    a_			)			)
  
  						lowerCAmelCase_     :							Dict             =    outputs.hidden_states
  
  						lowerCAmelCase_     :							int             =    getattr(
  						    self.model_tester		,    "expected_num_hidden_layers"		,    self.model_tester.num_hidden_layers + 1			)
  						self.assertEqual(len(a_			)		,    a_			)
  
  						self.assertListEqual(
  						    list(hidden_states[0].shape[-2:]			)		,    [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]		,    )
  
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							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_     :							Tuple             =    True
  
  						check_hidden_states_output(a_		,    a_		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				lowerCAmelCase_     :							Tuple             =    _config_zero_init(a_			)
  				for model_class in self.all_model_classes:
  						lowerCAmelCase_     :							Dict             =    model_class(config=a_			)
  						for name, param in model.named_parameters():
  								if isinstance(a_		,    (nn.Linear, nn.Convad, nn.LayerNorm)			):
  										if 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'''		,    )
  
  
  
  
  
  		@unittest.skip(reason="Retain_grad is tested in individual model tests"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						)		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[Any]             =    "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
  		lowerCAmelCase_     :							Optional[int]             =    Image.open(requests.get(__snake_case   , stream=__snake_case ).raw ).convert("RGB" )
  		return im
  
  
  
  @require_vision
  @require_torch
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			int			):
  				lowerCAmelCase_     :							Any             =    "alibaba-damo/mgp-str-base"
  				lowerCAmelCase_     :							Dict             =    MgpstrForSceneTextRecognition.from_pretrained(a_			).to(a_			)
  				lowerCAmelCase_     :							List[str]             =    MgpstrProcessor.from_pretrained(a_			)
  
  				lowerCAmelCase_     :							int             =    prepare_img()
  				lowerCAmelCase_     :							Any             =    processor(images=a_		,    return_tensors="pt"			).pixel_values.to(a_			)
  
  				# forward pass
  				with torch.no_grad():
  						lowerCAmelCase_     :							str             =    model(a_			)
  
  				# verify the logits
  				self.assertEqual(outputs.logits[0].shape		,    torch.Size((1, 27, 38)			)			)
  
  				lowerCAmelCase_     :							Dict             =    processor.batch_decode(outputs.logits			)
  				lowerCAmelCase_     :							Tuple             =    "ticket"
  
  				self.assertEqual(out_strs["generated_text"][0]		,    a_			)
  
  				lowerCAmelCase_     :							List[Any]             =    torch.tensor(
  				    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]		,    device=a_		,    )
  
  				self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]		,    a_		,    atol=1e-4			)			)
  
  
  
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
  