


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


snake_case__				:	Any					       =					8


def    __lowerCamelCase   (      A__						: Union[str, Any]						,						A__						: int=BITS					)     ->    Any:
   lowerCamelCase_						:   Union[str, Any]		= x.device

   lowerCamelCase_						:   Tuple		= (x * 255).int().clamp(0						,						255					)

   lowerCamelCase_						:   Any		= 2 ** torch.arange(bits - 1						,						-1						,						-1						,						device=A__					)
   lowerCamelCase_						:   Any		= rearrange(A__						,						"""d -> d 1 1"""					)
   lowerCamelCase_						:   Union[str, Any]		= rearrange(A__						,						"""b c h w -> b c 1 h w"""					)

   lowerCamelCase_						:   Optional[Any]		= ((x & mask) != 0).float()
   lowerCamelCase_						:   Optional[int]		= rearrange(A__						,						"""b c d h w -> b (c d) h w"""					)
   lowerCamelCase_						:   List[str]		= bits * 2 - 1
   return bits


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Optional[int]=BITS					)     ->    Any:
   lowerCamelCase_						:   List[str]		= x.device

   lowerCamelCase_						:   Union[str, Any]		= (x > 0).int()
   lowerCamelCase_						:   Tuple		= 2 ** torch.arange(bits - 1						,						-1						,						-1						,						device=A__						,						dtype=torch.intaa					)

   lowerCamelCase_						:   Optional[int]		= rearrange(A__						,						"""d -> d 1 1"""					)
   lowerCamelCase_						:   str		= rearrange(A__						,						"""b (c d) h w -> b c d h w"""						,						d=8					)
   lowerCamelCase_						:   Union[str, Any]		= reduce(x * mask						,						"""b c d h w -> b c h w"""						,						"""sum"""					)
   return (dec / 255).clamp(0.0						,						1.0					)


def    __lowerCamelCase   (      self						: Optional[Any]						,						A__						: torch.FloatTensor						,						A__						: int						,						A__						: torch.FloatTensor						,						A__						: float = 0.0						,						A__						: bool = True						,						A__						: List[Any]=None						,						A__						: bool = True						,						)     ->    Union[DDIMSchedulerOutput, Tuple]:
   if self.num_inference_steps is None:
      raise ValueError(
          """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""					)

   # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
   # Ideally, read DDIM paper in-detail understanding

   # Notation (<variable name> -> <name in paper>
   # - pred_noise_t -> e_theta(x_t, t)
   # - pred_original_sample -> f_theta(x_t, t) or x_0
   # - std_dev_t -> sigma_t
   # - eta -> η
   # - pred_sample_direction -> "direction pointing to x_t"
   # - pred_prev_sample -> "x_t-1"

   # 1. get previous step value (=t-1)
   lowerCamelCase_						:   Optional[Any]		= timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   lowerCamelCase_						:   Dict		= self.alphas_cumprod[timestep]
   lowerCamelCase_						:   List[str]		= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

   lowerCamelCase_						:   Union[str, Any]		= 1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   lowerCamelCase_						:   Optional[int]		= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   lowerCamelCase_						:   List[Any]		= self.bit_scale
   if self.config.clip_sample:
      lowerCamelCase_						:   List[Any]		= torch.clamp(A__						,						-scale						,						A__					)

   # 5. compute variance: "sigma_t(η)" -> see formula (16)
   # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
   lowerCamelCase_						:   Optional[int]		= self._get_variance(A__						,						A__					)
   lowerCamelCase_						:   Optional[Any]		= eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      lowerCamelCase_						:   Any		= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

   # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   lowerCamelCase_						:   Tuple		= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

   # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   lowerCamelCase_						:   Optional[int]		= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if eta > 0:
      # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
      lowerCamelCase_						:   int		= model_output.device if torch.is_tensor(A__					) else """cpu"""
      lowerCamelCase_						:   Optional[Any]		= torch.randn(model_output.shape						,						dtype=model_output.dtype						,						generator=A__					).to(A__					)
      lowerCamelCase_						:   Any		= self._get_variance(A__						,						A__					) ** 0.5 * eta * noise

      lowerCamelCase_						:   Optional[int]		= prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=A__						,						pred_original_sample=A__					)


def    __lowerCamelCase   (      self						: Any						,						A__						: torch.FloatTensor						,						A__						: int						,						A__						: torch.FloatTensor						,						A__						: List[Any]="epsilon"						,						A__						: Any=None						,						A__						: bool = True						,						)     ->    Union[DDPMSchedulerOutput, Tuple]:
   lowerCamelCase_						:   Optional[int]		= timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
      lowerCamelCase_,	lowerCamelCase_						:   str		= torch.split(A__						,						sample.shape[1]						,						dim=1					)
   else:
      lowerCamelCase_						:   Tuple		= None

   # 1. compute alphas, betas
   lowerCamelCase_						:   Tuple		= self.alphas_cumprod[t]
   lowerCamelCase_						:   Tuple		= self.alphas_cumprod[t - 1] if t > 0 else self.one
   lowerCamelCase_						:   Optional[int]		= 1 - alpha_prod_t
   lowerCamelCase_						:   Optional[int]		= 1 - alpha_prod_t_prev

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if prediction_type == "epsilon":
      lowerCamelCase_						:   Tuple		= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif prediction_type == "sample":
      lowerCamelCase_						:   Union[str, Any]		= model_output
   else:
      raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''					)

   # 3. Clip "predicted x_0"
   lowerCamelCase_						:   Tuple		= self.bit_scale
   if self.config.clip_sample:
      lowerCamelCase_						:   Tuple		= torch.clamp(A__						,						-scale						,						A__					)

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   lowerCamelCase_						:   Any		= (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
   lowerCamelCase_						:   Optional[int]		= self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   lowerCamelCase_						:   Union[str, Any]		= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   lowerCamelCase_						:   Union[str, Any]		= 0
   if t > 0:
      lowerCamelCase_						:   str		= torch.randn(
          model_output.size()						,						dtype=model_output.dtype						,						layout=model_output.layout						,						generator=A__					).to(model_output.device					)
      lowerCamelCase_						:   Dict		= (self._get_variance(A__						,						predicted_variance=A__					) ** 0.5) * noise

   lowerCamelCase_						:   Optional[int]		= pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=A__						,						pred_original_sample=A__					)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       def __init__(						self				:				Dict		,				__a				:				UNetaDConditionModel		,				__a				:				Union[DDIMScheduler, DDPMScheduler]		,				__a				:				Optional[float] = 1.0		,				)					->Any:
          super().__init__()
          lowerCamelCase_						:   str		= bit_scale
          lowerCamelCase_						:   Any		= (
              ddim_bit_scheduler_step if isinstance(__a		,				__a							) else ddpm_bit_scheduler_step
          )

          self.register_modules(unet=__a		,				scheduler=__a							)




       @torch.no_grad()
       def __call__(						self				:				Optional[int]		,				__a				:				Optional[int] = 256		,				__a				:				Optional[int] = 256		,				__a				:				Optional[int] = 50		,				__a				:				Optional[torch.Generator] = None		,				__a				:				Optional[int] = 1		,				__a				:				Optional[str] = "pil"		,				__a				:				bool = True		,				**__a				:				int		,				)					->Union[Tuple, ImagePipelineOutput]:
          lowerCamelCase_						:   List[str]		= torch.randn(
              (batch_size, self.unet.config.in_channels, height, width)		,				generator=__a		,				)
          lowerCamelCase_						:   List[str]		= decimal_to_bits(__a							) * self.bit_scale
          lowerCamelCase_						:   Any		= latents.to(self.device							)

          self.scheduler.set_timesteps(__a							)

          for t in self.progress_bar(self.scheduler.timesteps							):
             # predict the noise residual
             lowerCamelCase_						:   Any		= self.unet(__a		,				__a							).sample

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase_						:   Any		= self.scheduler.step(__a		,				__a		,				__a							).prev_sample

          lowerCamelCase_						:   List[str]		= bits_to_decimal(__a							)

          if output_type == "pil":
             lowerCamelCase_						:   List[str]		= self.numpy_to_pil(__a							)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=__a							)









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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__				:	Tuple					       =					logging.get_logger(__name__)


if is_vision_available():
      import PIL





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["pixel_values"]



       def __init__(						self				:				List[str]		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				bool = True		,				__a				:				Union[int, float] = 1 / 255		,				__a				:				bool = True		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = True		,				**__a				:				str		,				)					->None:
          super().__init__(**__a							)
          lowerCamelCase_						:   Any		= size if size is not None else {"""shortest_edge""": 224}
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          lowerCamelCase_						:   Any		= get_size_dict(__a		,				default_to_square=__a		,				param_name="""crop_size"""							)

          lowerCamelCase_						:   List[Any]		= do_resize
          lowerCamelCase_						:   str		= size
          lowerCamelCase_						:   Any		= resample
          lowerCamelCase_						:   List[Any]		= do_center_crop
          lowerCamelCase_						:   Any		= crop_size
          lowerCamelCase_						:   List[Any]		= do_rescale
          lowerCamelCase_						:   Tuple		= rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize
          lowerCamelCase_						:   Any		= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          lowerCamelCase_						:   int		= image_std if image_std is not None else OPENAI_CLIP_STD
          lowerCamelCase_						:   Tuple		= do_convert_rgb



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->np.ndarray:
          lowerCamelCase_						:   Optional[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          if "shortest_edge" not in size:
             raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
          lowerCamelCase_						:   Dict		= get_resize_output_image_size(__a		,				size=size["""shortest_edge"""]		,				default_to_square=__a							)
          return resize(__a		,				size=__a		,				resample=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[Any]		,				)					->np.ndarray:
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a							)
          if "height" not in size or "width" not in size:
             raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
          return center_crop(__a		,				size=(size["""height"""], size["""width"""])		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				np.ndarray		,				__a				:				Union[int, float]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->Dict:
          return rescale(__a		,				scale=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				np.ndarray		,				__a				:				Union[float, List[float]]		,				__a				:				Union[float, List[float]]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[str]		,				)					->np.ndarray:
          return normalize(__a		,				mean=__a		,				std=__a		,				data_format=__a		,				**__a							)




       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				ImageInput		,				__a				:				bool = None		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = None		,				__a				:				bool = None		,				__a				:				int = None		,				__a				:				bool = None		,				__a				:				float = None		,				__a				:				bool = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = None		,				__a				:				Optional[Union[str, TensorType]] = None		,				__a				:				Optional[ChannelDimension] = ChannelDimension.FIRST		,				**__a				:				List[str]		,				)					->PIL.Image.Image:
          lowerCamelCase_						:   List[Any]		= do_resize if do_resize is not None else self.do_resize
          lowerCamelCase_						:   Any		= size if size is not None else self.size
          lowerCamelCase_						:   str		= get_size_dict(__a		,				param_name="""size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= resample if resample is not None else self.resample
          lowerCamelCase_						:   List[str]		= do_center_crop if do_center_crop is not None else self.do_center_crop
          lowerCamelCase_						:   List[Any]		= crop_size if crop_size is not None else self.crop_size
          lowerCamelCase_						:   Optional[int]		= get_size_dict(__a		,				param_name="""crop_size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[Any]		= do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase_						:   List[str]		= rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize if do_normalize is not None else self.do_normalize
          lowerCamelCase_						:   Dict		= image_mean if image_mean is not None else self.image_mean
          lowerCamelCase_						:   Tuple		= image_std if image_std is not None else self.image_std
          lowerCamelCase_						:   Union[str, Any]		= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowerCamelCase_						:   Union[str, Any]		= make_list_of_images(__a							)

          if not valid_images(__a							):
             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:
             raise ValueError("""Size 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."""							)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
             lowerCamelCase_						:   str		= [convert_to_rgb(__a							) for image in images]

          # All transformations expect numpy arrays.
          lowerCamelCase_						:   str		= [to_numpy_array(__a							) for image in images]

          if do_resize:
             lowerCamelCase_						:   int		= [self.resize(image=__a		,				size=__a		,				resample=__a							) for image in images]

          if do_center_crop:
             lowerCamelCase_						:   Dict		= [self.center_crop(image=__a		,				size=__a							) for image in images]

          if do_rescale:
             lowerCamelCase_						:   List[str]		= [self.rescale(image=__a		,				scale=__a							) for image in images]

          if do_normalize:
             lowerCamelCase_						:   Tuple		= [self.normalize(image=__a		,				mean=__a		,				std=__a							) for image in images]

          lowerCamelCase_						:   Tuple		= [to_channel_dimension_format(__a		,				__a							) for image in images]

          lowerCamelCase_						:   Tuple		= {"""pixel_values""": images}
          return BatchFeature(data=__a		,				tensor_type=__a							)





