



from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


UpperCamelCase_									=   logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : torch.FloatTensor







class       _snake_case				(     __snake_case ,							__snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						1


   @register_to_config
   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     int = 40  ,lowerCamelCase_:     float = 0.0_0_2  ,lowerCamelCase_:     float = 8_0.0  ,lowerCamelCase_:     float = 0.5  ,lowerCamelCase_:     float = 1.0  ,lowerCamelCase_:     float = 7.0  ,lowerCamelCase_:     bool = True  ,)	->      List[Any]:
       # standard deviation of the initial noise distribution
       UpperCAmelCase_     :       Tuple  		=					sigma_max

       UpperCAmelCase_     :       int  		=					np.linspace(0  ,1  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					self._convert_to_karras(lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					self.sigma_to_t(lowerCamelCase_							)

       # setable values
       UpperCAmelCase_     :       Optional[Any]  		=					None
       UpperCAmelCase_     :       Any  		=					torch.from_numpy(lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					torch.from_numpy(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					False
       UpperCAmelCase_     :       Dict  		=					False


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[str]=None							)	->      Optional[Any]:
       if schedule_timesteps is None:
           UpperCAmelCase_     :       str  		=					self.timesteps

       UpperCAmelCase_     :       str  		=					(schedule_timesteps == timestep).nonzero()
       return indices.item()


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Union[float, torch.FloatTensor]							)	->      torch.FloatTensor:
       # Get sigma corresponding to timestep
       if isinstance(lowerCamelCase_  ,torch.Tensor							):
           UpperCAmelCase_     :       Any  		=					timestep.to(self.timesteps.device							)
       UpperCAmelCase_     :       Optional[Any]  		=					self.index_for_timestep(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					self.sigmas[step_idx]

       UpperCAmelCase_     :       Tuple  		=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       UpperCAmelCase_     :       Any  		=					True
       return sample


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[float, np.ndarray]							)	->      Tuple:
       if not isinstance(lowerCamelCase_  ,np.ndarray							):
           UpperCAmelCase_     :       str  		=					np.array(lowerCamelCase_  ,dtype=np.floataa							)

       UpperCAmelCase_     :       Dict  		=					1000 * 0.2_5 * np.log(sigmas + 1e-44							)

       return timesteps


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     Union[str, torch.device] = None  ,lowerCamelCase_:     Optional[List[int]] = None  ,)	->      Optional[Any]:
       if num_inference_steps is None and timesteps is None:
           raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

       if num_inference_steps is not None and timesteps is not None:
           raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""							)

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
           for i in range(1  ,len(lowerCamelCase_							)							):
               if timesteps[i] >= timesteps[i - 1]:
                   raise ValueError("""`timesteps` must be in descending order."""							)

           if timesteps[0] >= self.config.num_train_timesteps:
               raise ValueError(
                   F'''`timesteps` must start before `self.config.train_timesteps`:'''
                   F''' {self.config.num_train_timesteps}.'''							)

           UpperCAmelCase_     :       List[Any]  		=					np.array(lowerCamelCase_  ,dtype=np.intaa							)
           UpperCAmelCase_     :       Tuple  		=					True
       else:
           if num_inference_steps > self.config.num_train_timesteps:
               raise ValueError(
                   F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                   F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                   F''' maximal {self.config.num_train_timesteps} timesteps.'''							)

           UpperCAmelCase_     :       Any  		=					num_inference_steps

           UpperCAmelCase_     :       Any  		=					self.config.num_train_timesteps // self.num_inference_steps
           UpperCAmelCase_     :       Optional[int]  		=					(np.arange(0  ,lowerCamelCase_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
           UpperCAmelCase_     :       int  		=					False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       UpperCAmelCase_     :       List[str]  		=					self.config.num_train_timesteps
       UpperCAmelCase_     :       Any  		=					timesteps[::-1].copy()
       UpperCAmelCase_     :       int  		=					ramp / (num_train_timesteps - 1)
       UpperCAmelCase_     :       Tuple  		=					self._convert_to_karras(lowerCamelCase_							)
       UpperCAmelCase_     :       Dict  		=					self.sigma_to_t(lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
       UpperCAmelCase_     :       Union[str, Any]  		=					torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)

       if str(lowerCamelCase_							).startswith("""mps"""							):
           # mps does not support float64
           UpperCAmelCase_     :       Optional[Any]  		=					torch.from_numpy(lowerCamelCase_							).to(lowerCamelCase_  ,dtype=torch.floataa							)
       else:
           UpperCAmelCase_     :       Optional[int]  		=					torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)


   def       A__    (    self:     str  ,lowerCamelCase_:     Any							)	->      Optional[Any]:
       UpperCAmelCase_     :       float  		=					self.config.sigma_min
       UpperCAmelCase_     :       float  		=					self.config.sigma_max

       UpperCAmelCase_     :       Tuple  		=					self.config.rho
       UpperCAmelCase_     :       str  		=					sigma_min ** (1 / rho)
       UpperCAmelCase_     :       Optional[int]  		=					sigma_max ** (1 / rho)
       UpperCAmelCase_     :       str  		=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     int							)	->      Tuple:
       UpperCAmelCase_     :       List[Any]  		=					self.config.sigma_data

       UpperCAmelCase_     :       int  		=					sigma_data**2 / (sigma**2 + sigma_data**2)
       UpperCAmelCase_     :       str  		=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     List[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       List[Any]  		=					self.config.sigma_min
       UpperCAmelCase_     :       List[str]  		=					self.config.sigma_data

       UpperCAmelCase_     :       Union[str, Any]  		=					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       UpperCAmelCase_     :       Any  		=					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Union[float, torch.FloatTensor]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     bool = True  ,)	->      Union[CMStochasticIterativeSchedulerOutput, Tuple]:

       if (
           isinstance(lowerCamelCase_  ,lowerCamelCase_							)
           or isinstance(lowerCamelCase_  ,torch.IntTensor							)
           or isinstance(lowerCamelCase_  ,torch.LongTensor							)
       ):
           raise ValueError(
               (
                   """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                   F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                   """ one of the `scheduler.timesteps` as a timestep."""
               )  ,)

       if not self.is_scale_input_called:
           logger.warning(
               """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
               """See `StableDiffusionPipeline` for a usage example."""							)

       if isinstance(lowerCamelCase_  ,torch.Tensor							):
           UpperCAmelCase_     :       List[str]  		=					timestep.to(self.timesteps.device							)

       UpperCAmelCase_     :       Optional[Any]  		=					self.config.sigma_min
       UpperCAmelCase_     :       List[str]  		=					self.config.sigma_max

       UpperCAmelCase_     :       int  		=					self.index_for_timestep(lowerCamelCase_							)

       # sigma_next corresponds to next_t in original implementation
       UpperCAmelCase_     :       Optional[Any]  		=					self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
           UpperCAmelCase_     :       Dict  		=					self.sigmas[step_index + 1]
       else:
           # Set sigma_next to sigma_min
           UpperCAmelCase_     :       int  		=					self.sigmas[-1]

       # Get scalings for boundary conditions
       UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					self.get_scalings_for_boundary_condition(lowerCamelCase_							)

       # 1. Denoise model output using boundary conditions
       UpperCAmelCase_     :       Optional[Any]  		=					c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
           UpperCAmelCase_     :       int  		=					denoised.clamp(-1  ,1							)

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps							) > 1:
           UpperCAmelCase_     :       Optional[int]  		=					randn_tensor(
               model_output.shape  ,dtype=model_output.dtype  ,device=model_output.device  ,generator=lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Union[str, Any]  		=					torch.zeros_like(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					noise * self.config.s_noise

       UpperCAmelCase_     :       str  		=					sigma_next.clamp(min=lowerCamelCase_  ,max=lowerCamelCase_							)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       UpperCAmelCase_     :       Tuple  		=					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
           return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCamelCase_							)


   def       A__    (    self:     str  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     torch.FloatTensor  ,)	->      torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCAmelCase_     :       int  		=					self.sigmas.to(device=original_samples.device  ,dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_							):
           # mps does not support float64
           UpperCAmelCase_     :       List[str]  		=					self.timesteps.to(original_samples.device  ,dtype=torch.floataa							)
           UpperCAmelCase_     :       Dict  		=					timesteps.to(original_samples.device  ,dtype=torch.floataa							)
       else:
           UpperCAmelCase_     :       int  		=					self.timesteps.to(original_samples.device							)
           UpperCAmelCase_     :       Tuple  		=					timesteps.to(original_samples.device							)

       UpperCAmelCase_     :       Tuple  		=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       UpperCAmelCase_     :       Any  		=					sigmas[step_indices].flatten()
       while len(sigma.shape							) < len(original_samples.shape							):
           UpperCAmelCase_     :       Dict  		=					sigma.unsqueeze(-1							)

       UpperCAmelCase_     :       List[str]  		=					original_samples + noise * sigma
       return noisy_samples






   def __len__(    self:     Tuple							)	->      Optional[Any]:
       return self.config.num_train_timesteps


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_				:							List[str]       =    logging.get_logger(__name__)







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			["""pixel_values"""]

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1 / 255							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = True							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   size if size is not None else {'height': 346, 'width': 346}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else {'height': 289, 'width': 289}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

            __snake_case						       =   do_resize
            __snake_case						       =   size
            __snake_case						       =   resample
            __snake_case						       =   do_center_crop
            __snake_case						       =   crop_size
            __snake_case						       =   do_rescale
            __snake_case						       =   rescale_factor
            __snake_case						       =   rescale_offset
            __snake_case						       =   do_normalize
            __snake_case						       =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __snake_case						       =   image_std if image_std is not None else IMAGENET_STANDARD_STD
            __snake_case						       =   include_top

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            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(
                SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       resample=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            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(SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            if offset:
                   __snake_case						       =   (image - 1_2_7.5) * scale
                   if data_format is not None:
                          __snake_case						       =   to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   rescaled_image.astype(np.floataa    )
            else:
                   __snake_case						       =   rescale(SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
            return rescaled_image

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            return normalize(SCREAMING_SNAKE_CASE_							,       mean=SCREAMING_SNAKE_CASE_							,       std=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   do_resize if do_resize is not None else self.do_resize
            __snake_case						       =   resample if resample is not None else self.resample
            __snake_case						       =   do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case						       =   do_rescale if do_rescale is not None else self.do_rescale
            __snake_case						       =   rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case						       =   rescale_offset if rescale_offset is not None else self.rescale_offset
            __snake_case						       =   do_normalize if do_normalize is not None else self.do_normalize
            __snake_case						       =   image_mean if image_mean is not None else self.image_mean
            __snake_case						       =   image_std if image_std is not None else self.image_std
            __snake_case						       =   include_top if include_top is not None else self.include_top

            __snake_case						       =   size if size is not None else self.size
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else self.crop_size
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

            __snake_case						       =   make_list_of_images(SCREAMING_SNAKE_CASE_    )

            if not valid_images(SCREAMING_SNAKE_CASE_    ):
                   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.
            __snake_case						       =   [to_numpy_array(SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_resize:
                   __snake_case						       =   [self.resize(image=SCREAMING_SNAKE_CASE_							,       size=SCREAMING_SNAKE_CASE_							,       resample=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_center_crop:
                   __snake_case						       =   [self.center_crop(image=SCREAMING_SNAKE_CASE_							,       size=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_rescale:
                   __snake_case						       =   [self.rescale(image=SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       offset=SCREAMING_SNAKE_CASE_    ) for image in images]

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

            if include_top:
                   __snake_case						       =   [self.normalize(image=SCREAMING_SNAKE_CASE_							,       mean=[0, 0, 0]							,       std=SCREAMING_SNAKE_CASE_    ) for image in images]

            __snake_case						       =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) for image in images]

            __snake_case						       =   {'pixel_values': images}
            return BatchFeature(data=SCREAMING_SNAKE_CASE_							,       tensor_type=SCREAMING_SNAKE_CASE_    )
