



"""simple docstring"""





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


lowerCamelCase      =		8
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=BITS   ):
      UpperCAmelCase_    				=							x.device

      UpperCAmelCase_    				=							(x * 255).int().clamp(0			,    255   )

      UpperCAmelCase_    				=							2 ** torch.arange(bits - 1			,    -1			,    -1			,    device=lowerCAmelCase__   )
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "d -> d 1 1"   )
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "b c h w -> b c 1 h w"   )

      UpperCAmelCase_    				=							((x & mask) != 0).float()
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "b c d h w -> b (c d) h w"   )
      UpperCAmelCase_    				=							bits * 2 - 1
      return bits
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=BITS   ):
      UpperCAmelCase_    				=							x.device

      UpperCAmelCase_    				=							(x > 0).int()
      UpperCAmelCase_    				=							2 ** torch.arange(bits - 1			,    -1			,    -1			,    device=lowerCAmelCase__			,    dtype=torch.intaa   )

      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "d -> d 1 1"   )
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "b (c d) h w -> b c d h w"			,    d=8   )
      UpperCAmelCase_    				=							reduce(x * mask			,    "b c d h w -> b c h w"			,    "sum"   )
      return (dec / 255).clamp(0.0			,    1.0   )
def      a__			(   self			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = 0.0			,    lowerCAmelCase__ = True			,    lowerCAmelCase__=None			,    lowerCAmelCase__ = True			,    ):
      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)
      UpperCAmelCase_    				=							timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      UpperCAmelCase_    				=							self.alphas_cumprod[timestep]
      UpperCAmelCase_    				=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      UpperCAmelCase_    				=							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
      UpperCAmelCase_    				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      UpperCAmelCase_    				=							self.bit_scale
      if self.config.clip_sample:
            UpperCAmelCase_    				=							torch.clamp(lowerCAmelCase__			,    -scale			,    lowerCAmelCase__   )

      # 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)
      UpperCAmelCase_    				=							self._get_variance(lowerCAmelCase__			,    lowerCAmelCase__   )
      UpperCAmelCase_    				=							eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            UpperCAmelCase_    				=							(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
      UpperCAmelCase_    				=							(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
      UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							model_output.device if torch.is_tensor(lowerCAmelCase__   ) else "cpu"
            UpperCAmelCase_    				=							torch.randn(model_output.shape			,    dtype=model_output.dtype			,    generator=lowerCAmelCase__   ).to(lowerCAmelCase__   )
            UpperCAmelCase_    				=							self._get_variance(lowerCAmelCase__			,    lowerCAmelCase__   ) ** 0.5 * eta * noise

            UpperCAmelCase_    				=							prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__			,    pred_original_sample=lowerCAmelCase__   )
def      a__			(   self			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__="epsilon"			,    lowerCAmelCase__=None			,    lowerCAmelCase__ = True			,    ):
      UpperCAmelCase_    				=							timestep

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

      # 1. compute alphas, betas
      UpperCAmelCase_    				=							self.alphas_cumprod[t]
      UpperCAmelCase_    				=							self.alphas_cumprod[t - 1] if t > 0 else self.one
      UpperCAmelCase_    				=							1 - alpha_prod_t
      UpperCAmelCase_    				=							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":
            UpperCAmelCase_    				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            UpperCAmelCase_    				=							model_output
      else:
            raise ValueError(f"""Unsupported prediction_type {prediction_type}."""   )

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

      # 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
      UpperCAmelCase_    				=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      UpperCAmelCase_    				=							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
      UpperCAmelCase_    				=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      UpperCAmelCase_    				=							0
      if t > 0:
            UpperCAmelCase_    				=							torch.randn(
                model_output.size()			,    dtype=model_output.dtype			,    layout=model_output.layout			,    generator=lowerCAmelCase__   ).to(model_output.device   )
            UpperCAmelCase_    				=							(self._get_variance(lowerCAmelCase__			,    predicted_variance=lowerCAmelCase__   ) ** 0.5) * noise

      UpperCAmelCase_    				=							pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__			,    pred_original_sample=lowerCAmelCase__   )


class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       def __init__(							self    :						Union[str, Any]	,       _UpperCAmelCase    :						UNetaDConditionModel	,       _UpperCAmelCase    :						Union[DDIMScheduler, DDPMScheduler]	,       _UpperCAmelCase    :						Optional[float] = 1.0	,       )				->		str:


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_    				=							bit_scale
             UpperCAmelCase_    				=							(
                 ddim_bit_scheduler_step if isinstance(_UpperCAmelCase	,       _UpperCAmelCase					) else ddpm_bit_scheduler_step
             )

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







       @torch.no_grad()
       def __call__(							self    :						Dict	,       _UpperCAmelCase    :						Optional[int] = 256	,       _UpperCAmelCase    :						Optional[int] = 256	,       _UpperCAmelCase    :						Optional[int] = 50	,       _UpperCAmelCase    :						Optional[torch.Generator] = None	,       _UpperCAmelCase    :						Optional[int] = 1	,       _UpperCAmelCase    :						Optional[str] = "pil"	,       _UpperCAmelCase    :						bool = True	,       **_UpperCAmelCase    :						List[Any]	,       )				->		Union[Tuple, ImagePipelineOutput]:


             '''simple docstring'''




             UpperCAmelCase_    				=							torch.randn(
                 (batch_size, self.unet.config.in_channels, height, width)	,       generator=_UpperCAmelCase	,       )
             UpperCAmelCase_    				=							decimal_to_bits(_UpperCAmelCase					) * self.bit_scale
             UpperCAmelCase_    				=							latents.to(self.device					)

             self.scheduler.set_timesteps(_UpperCAmelCase					)

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

                   # compute the previous noisy sample x_t -> x_t-1
                   UpperCAmelCase_    				=							self.scheduler.step(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					).prev_sample

             UpperCAmelCase_    				=							bits_to_decimal(_UpperCAmelCase					)

             if output_type == "pil":
                   UpperCAmelCase_    				=							self.numpy_to_pil(_UpperCAmelCase					)

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=_UpperCAmelCase					)









"""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,
    get_resize_output_image_size,
    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      =		logging.get_logger(__name__)
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						str	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[int, float] = 1 / 255	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"shortest_edge": 384}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             # Default value set here for backwards compatibility where the value in config is None
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else 224 / 256
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else IMAGENET_STANDARD_STD



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						float	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BICUBIC	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "shortest_edge" not in size:
                   raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
             UpperCAmelCase_    				=							size["shortest_edge"]

             if shortest_edge < 384:
                   # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                   UpperCAmelCase_    				=							int(shortest_edge / crop_pct					)
                   UpperCAmelCase_    				=							get_resize_output_image_size(_UpperCAmelCase	,       size=_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
                   UpperCAmelCase_    				=							resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
                   # then crop to (shortest_edge, shortest_edge)
                   return center_crop(image=_UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
             else:
                   # warping (no cropping) when evaluated at 384 or larger
                   return resize(
                       _UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[int, float]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[Any]	,       )				->		Any:


             '''simple docstring'''




             return rescale(_UpperCAmelCase	,       scale=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[str]	,       )				->		np.ndarray:


             '''simple docstring'''




             return normalize(_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[str, TensorType]] = None	,       _UpperCAmelCase    :						ChannelDimension = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else self.crop_pct
             UpperCAmelCase_    				=							resample if resample is not None else self.resample
             UpperCAmelCase_    				=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_    				=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_    				=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_    				=							image_std if image_std is not None else self.image_std

             UpperCAmelCase_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                   raise ValueError("crop_pct must be specified if size < 384."					)

             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.
             UpperCAmelCase_    				=							[to_numpy_array(_UpperCAmelCase					) for image in images]

             if do_resize:
                   UpperCAmelCase_    				=							[self.resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       crop_pct=_UpperCAmelCase	,       resample=_UpperCAmelCase					) for image in images]

             if do_rescale:
                   UpperCAmelCase_    				=							[self.rescale(image=_UpperCAmelCase	,       scale=_UpperCAmelCase					) for image in images]

             if do_normalize:
                   UpperCAmelCase_    				=							[self.normalize(image=_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							[to_channel_dimension_format(_UpperCAmelCase	,       _UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							{"pixel_values": images}
             return BatchFeature(data=_UpperCAmelCase	,       tensor_type=_UpperCAmelCase					)




