code: 
  
  
  
  
  '''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		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  torch.FloatTensor
        lowerCAmelCase__      :  torch.FloatTensor
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A=0.999						,				A="cosine"						,				) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if alpha_transform_type == "cosine":
  
                def alpha_bar_fn(A ):
                       return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
  
         elif alpha_transform_type == "exp":
  
                def alpha_bar_fn(A ):
                       return math.exp(t * -12.0 )
  
         else:
                raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
  
         lowercase__       =				[]
         for i in range(A ):
                lowercase__       =				i / num_diffusion_timesteps
                lowercase__       =				(i + 1) / num_diffusion_timesteps
                betas.append(min(1 - alpha_bar_fn(A ) / alpha_bar_fn(A )						,				A ) )
         return torch.tensor(A						,				dtype=torch.floataa )
  
  class 						__lowerCAmelCase		(lowercase_						,  lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  List[str]						      =							1
  
  
  
        @register_to_config
        def __init__(self					:					Tuple     ,		UpperCamelCase					:					int = 1000     ,		UpperCamelCase					:					float = 0.00_01     ,		UpperCamelCase					:					float = 0.02     ,		UpperCamelCase					:					str = "linear"     ,		UpperCamelCase					:					float = 0.0     ,		UpperCamelCase					:					Optional[np.ndarray] = None     ,		UpperCamelCase					:					bool = True     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if trained_betas is not None:
                      lowercase__       =				torch.from_numpy(UpperCamelCase				)
               elif beta_schedule == "linear":
                      lowercase__       =				torch.linspace(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		dtype=torch.floataa				)
               elif beta_schedule == "scaled_linear":
                      # this schedule is very specific to the latent diffusion model.
                      lowercase__       =				(
                          torch.linspace(beta_start**0.5     ,		beta_end**0.5     ,		UpperCamelCase     ,		dtype=torch.floataa				) ** 2
                      )
               elif beta_schedule == "squaredcos_cap_v2":
                      # Glide cosine schedule
                      lowercase__       =				betas_for_alpha_bar(UpperCamelCase				)
               elif beta_schedule == "sigmoid":
                      # GeoDiff sigmoid schedule
                      lowercase__       =				torch.linspace(-6     ,		6     ,		UpperCamelCase				)
                      lowercase__       =				torch.sigmoid(UpperCamelCase				) * (beta_end - beta_start) + beta_start
               else:
                      raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"				)
  
               lowercase__       =				1.0 - self.betas
               lowercase__       =				torch.cumprod(self.alphas     ,		dim=0				)
               lowercase__       =				torch.tensor(1.0				)
  
               lowercase__       =				torch.tensor(1.0				)
  
               # standard deviation of the initial noise distribution
               lowercase__       =				1.0
  
               # setable values
               lowercase__       =				None
               lowercase__       =				torch.from_numpy(np.arange(0     ,		UpperCamelCase				)[::-1].copy()				)
  
               lowercase__       =				eta
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					Optional[int] = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return sample
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					int = 10     ,		UpperCamelCase					:					int = 10     ,		UpperCamelCase					:					Union[str, torch.device] = None     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				min(self.config.num_train_timesteps     ,		UpperCamelCase				)
               lowercase__       =				num_inference_steps
  
               lowercase__       =				[]
  
               lowercase__       =				{}
               for j in range(0     ,		num_inference_steps - jump_length     ,		UpperCamelCase				):
                      lowercase__       =				jump_n_sample - 1
  
               lowercase__       =				num_inference_steps
               while t >= 1:
                      lowercase__       =				t - 1
                      timesteps.append(UpperCamelCase				)
  
                      if jumps.get(UpperCamelCase     ,		0				) > 0:
                             lowercase__       =				jumps[t] - 1
                             for _ in range(UpperCamelCase				):
                                    lowercase__       =				t + 1
                                    timesteps.append(UpperCamelCase				)
  
               lowercase__       =				np.array(UpperCamelCase				) * (self.config.num_train_timesteps // self.num_inference_steps)
               lowercase__       =				torch.from_numpy(UpperCamelCase				).to(UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				t - self.config.num_train_timesteps // self.num_inference_steps
  
               lowercase__       =				self.alphas_cumprod[t]
               lowercase__       =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
               lowercase__       =				1 - alpha_prod_t
               lowercase__       =				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]
               lowercase__       =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
               return variance
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					Optional[torch.Generator] = None     ,		UpperCamelCase					:					bool = True     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				timestep
               lowercase__       =				timestep - self.config.num_train_timesteps // self.num_inference_steps
  
               # 1. compute alphas, betas
               lowercase__       =				self.alphas_cumprod[t]
               lowercase__       =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
               lowercase__       =				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
               lowercase__       =				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
               # 3. Clip "predicted x_0"
               if self.config.clip_sample:
                      lowercase__       =				torch.clamp(UpperCamelCase     ,		-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
               lowercase__       =				model_output.device
               lowercase__       =				randn_tensor(model_output.shape     ,		generator=UpperCamelCase     ,		device=UpperCamelCase     ,		dtype=model_output.dtype				)
               lowercase__       =				self.eta * self._get_variance(UpperCamelCase				) ** 0.5
  
               lowercase__       =				0
               if t > 0 and self.eta > 0:
                      lowercase__       =				std_dev_t * noise
  
               # 6. compute "direction pointing to x_t" of formula (12)
               # from https://arxiv.org/pdf/2010.02502.pdf
               lowercase__       =				(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
               lowercase__       =				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
               lowercase__       =				(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
               lowercase__       =				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=UpperCamelCase     ,		pred_original_sample=UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[str]=None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.config.num_train_timesteps // self.num_inference_steps
  
               for i in range(UpperCamelCase				):
                      lowercase__       =				self.betas[timestep + i]
                      if sample.device.type == "mps":
                             # randn does not work reproducibly on mps
                             lowercase__       =				randn_tensor(sample.shape     ,		dtype=sample.dtype     ,		generator=UpperCamelCase				)
                             lowercase__       =				noise.to(sample.device				)
                      else:
                             lowercase__       =				randn_tensor(sample.shape     ,		generator=UpperCamelCase     ,		device=sample.device     ,		dtype=sample.dtype				)
  
                      # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                      lowercase__       =				(1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
               return sample
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					torch.FloatTensor     ,		UpperCamelCase					:					torch.IntTensor     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''				)
  
  
  
  
  
  
        def __len__(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
   import PIL
  
  
  lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  List[str]						      =							["""pixel_values"""]
  
  
  
        def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[int, float] = 1 / 255     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					Optional[int]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(**UpperCamelCase				)
               lowercase__       =				size if size is not None else {'''height''': 346, '''width''': 346}
               lowercase__       =				get_size_dict(UpperCamelCase				)
               lowercase__       =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
               lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)
  
               lowercase__       =				do_resize
               lowercase__       =				size
               lowercase__       =				resample
               lowercase__       =				do_center_crop
               lowercase__       =				crop_size
               lowercase__       =				do_rescale
               lowercase__       =				rescale_factor
               lowercase__       =				rescale_offset
               lowercase__       =				do_normalize
               lowercase__       =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
               lowercase__       =				image_std if image_std is not None else IMAGENET_STANDARD_STD
               lowercase__       =				include_top
  
  
  
        def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Dict     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				get_size_dict(UpperCamelCase				)
               if "height" not in size or "width" not in size:
                      raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
               return resize(
                   UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		resample=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Union[str, Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				get_size_dict(UpperCamelCase				)
               if "height" not in size or "width" not in size:
                      raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
               return center_crop(UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[int, float]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if offset:
                      lowercase__       =				(image - 1_27.5) * scale
                      if data_format is not None:
                             lowercase__       =				to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				)
                      lowercase__       =				rescaled_image.astype(np.floataa				)
               else:
                      lowercase__       =				rescale(UpperCamelCase     ,		scale=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
               return rescaled_image
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return normalize(UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					ImageInput     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[str, Any]=None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					float = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[str, TensorType]] = None     ,		UpperCamelCase					:					ChannelDimension = ChannelDimension.FIRST     ,		**UpperCamelCase					:					List[Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				do_resize if do_resize is not None else self.do_resize
               lowercase__       =				resample if resample is not None else self.resample
               lowercase__       =				do_center_crop if do_center_crop is not None else self.do_center_crop
               lowercase__       =				do_rescale if do_rescale is not None else self.do_rescale
               lowercase__       =				rescale_factor if rescale_factor is not None else self.rescale_factor
               lowercase__       =				rescale_offset if rescale_offset is not None else self.rescale_offset
               lowercase__       =				do_normalize if do_normalize is not None else self.do_normalize
               lowercase__       =				image_mean if image_mean is not None else self.image_mean
               lowercase__       =				image_std if image_std is not None else self.image_std
               lowercase__       =				include_top if include_top is not None else self.include_top
  
               lowercase__       =				size if size is not None else self.size
               lowercase__       =				get_size_dict(UpperCamelCase				)
               lowercase__       =				crop_size if crop_size is not None else self.crop_size
               lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)
  
               lowercase__       =				make_list_of_images(UpperCamelCase				)
  
               if not valid_images(UpperCamelCase				):
                      raise ValueError(
                          '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                          '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)
  
               if do_resize and size is None or resample is None:
                      raise ValueError('''Size and resample must be specified if do_resize is True.'''				)
  
               if do_center_crop and crop_size is None:
                      raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)
  
               if do_rescale and rescale_factor is None:
                      raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)
  
               if do_normalize and (image_mean is None or image_std is None):
                      raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)
  
               # All transformations expect numpy arrays.
               lowercase__       =				[to_numpy_array(UpperCamelCase				) for image in images]
  
               if do_resize:
                      lowercase__       =				[self.resize(image=UpperCamelCase     ,		size=UpperCamelCase     ,		resample=UpperCamelCase				) for image in images]
  
               if do_center_crop:
                      lowercase__       =				[self.center_crop(image=UpperCamelCase     ,		size=UpperCamelCase				) for image in images]
  
               if do_rescale:
                      lowercase__       =				[self.rescale(image=UpperCamelCase     ,		scale=UpperCamelCase     ,		offset=UpperCamelCase				) for image in images]
  
               if do_normalize:
                      lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase				) for image in images]
  
               if include_top:
                      lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=[0, 0, 0]     ,		std=UpperCamelCase				) for image in images]
  
               lowercase__       =				[to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				) for image in images]
  
               lowercase__       =				{'''pixel_values''': images}
               return BatchFeature(data=UpperCamelCase     ,		tensor_type=UpperCamelCase				)
  
  
  
  
  