

'''simple docstring'''

import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	42
       lowerCAmelCase_				=	None






def 			__UpperCamelCase							(		lowercase__  :					Optional[int],   lowercase__  :					Tuple=0.999,   lowercase__  :					List[str]="cosine",   ):






     '''simple docstring'''





     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowercase__  :					Optional[Any]	):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowercase__  :					Optional[Any]	):
               return math.exp(t * -12.0	)

     else:
          raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

     __lowercase     						=[]
     for i in range(lowercase__	):
          __lowercase     						=i / num_diffusion_timesteps
          __lowercase     						=(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(lowercase__	) / alpha_bar_fn(lowercase__	),   lowercase__	)	)
     return torch.tensor(lowercase__,   dtype=torch.floataa	)







class 					lowerCAmelCase       ( A				,     A				):
       lowerCAmelCase_				=	[e.name for e in KarrasDiffusionSchedulers]
       lowerCAmelCase_				=	1







       @register_to_config
       def __init__(							self     :	str  , __lowercase     :	int = 1000  , __lowercase     :	float = 0.0_0_0_1  , __lowercase     :	float = 0.0_2  , __lowercase     :	str = "linear"  , __lowercase     :	Optional[Union[np.ndarray, List[float]]] = None  , __lowercase     :	str = "epsilon"  , __lowercase     :	str = "linear"  , __lowercase     :	Optional[bool] = False  , __lowercase     :	str = "linspace"  , __lowercase     :	int = 0  , ):







            """simple docstring"""







            if trained_betas is not None:
                 __lowercase     						=torch.tensor(__lowercase  , dtype=torch.floataa )
            elif beta_schedule == "linear":
                 __lowercase     						=torch.linspace(__lowercase  , __lowercase  , __lowercase  , 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  , __lowercase  , dtype=torch.floataa ) ** 2
                 )
            elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 __lowercase     						=betas_for_alpha_bar(__lowercase )
            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     						=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
            __lowercase     						=np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
            __lowercase     						=torch.from_numpy(__lowercase )

            # setable values
            __lowercase     						=None
            __lowercase     						=np.linspace(0  , num_train_timesteps - 1  , __lowercase  , dtype=__lowercase )[::-1].copy()
            __lowercase     						=torch.from_numpy(__lowercase )
            __lowercase     						=False
            __lowercase     						=use_karras_sigmas







       @property
       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            if self.config.timestep_spacing in ["linspace", "trailing"]:
                 return self.sigmas.max()

            return (self.sigmas.max() ** 2 + 1) ** 0.5







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	torch.FloatTensor  , __lowercase     :	Union[float, torch.FloatTensor] ):







            """simple docstring"""







            if isinstance(__lowercase  , torch.Tensor ):
                 __lowercase     						=timestep.to(self.timesteps.device )
            __lowercase     						=(self.timesteps == timestep).nonzero().item()
            __lowercase     						=self.sigmas[step_index]

            __lowercase     						=sample / ((sigma**2 + 1) ** 0.5)

            __lowercase     						=True
            return sample







       def      snake_case				(							self     :	List[str]  , __lowercase     :	int  , __lowercase     :	Union[str, torch.device] = None ):







            """simple docstring"""







            __lowercase     						=num_inference_steps

            # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
            if self.config.timestep_spacing == "linspace":
                 __lowercase     						=np.linspace(0  , self.config.num_train_timesteps - 1  , __lowercase  , dtype=__lowercase )[
                     ::-1
                 ].copy()
            elif self.config.timestep_spacing == "leading":
                 __lowercase     						=self.config.num_train_timesteps // self.num_inference_steps
                 # creates integer timesteps by multiplying by ratio
                 # casting to int to avoid issues when num_inference_step is power of 3
                 __lowercase     						=(np.arange(0  , __lowercase ) * step_ratio).round()[::-1].copy().astype(__lowercase )
                 timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                 __lowercase     						=self.config.num_train_timesteps / self.num_inference_steps
                 # creates integer timesteps by multiplying by ratio
                 # casting to int to avoid issues when num_inference_step is power of 3
                 __lowercase     						=(np.arange(self.config.num_train_timesteps  , 0  , -step_ratio )).round().copy().astype(__lowercase )
                 timesteps -= 1
            else:
                 raise ValueError(
                     f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

            __lowercase     						=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
            __lowercase     						=np.log(__lowercase )

            if self.config.interpolation_type == "linear":
                 __lowercase     						=np.interp(__lowercase  , np.arange(0  , len(__lowercase ) )  , __lowercase )
            elif self.config.interpolation_type == "log_linear":
                 __lowercase     						=torch.linspace(np.log(sigmas[-1] )  , np.log(sigmas[0] )  , num_inference_steps + 1 ).exp()
            else:
                 raise ValueError(
                     f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
                     ' \'linear\' or \'log_linear\'' )

            if self.use_karras_sigmas:
                 __lowercase     						=self._convert_to_karras(in_sigmas=__lowercase  , num_inference_steps=self.num_inference_steps )
                 __lowercase     						=np.array([self._sigma_to_t(__lowercase  , __lowercase ) for sigma in sigmas] )

            __lowercase     						=np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
            __lowercase     						=torch.from_numpy(__lowercase ).to(device=__lowercase )
            if str(__lowercase ).startswith('mps' ):
                 # mps does not support float64
                 __lowercase     						=torch.from_numpy(__lowercase ).to(__lowercase  , dtype=torch.floataa )
            else:
                 __lowercase     						=torch.from_numpy(__lowercase ).to(device=__lowercase )







       def      snake_case				(							self     :	int  , __lowercase     :	Optional[int]  , __lowercase     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=np.log(__lowercase )

            # get distribution
            __lowercase     						=log_sigma - log_sigmas[:, np.newaxis]

            # get sigmas range
            __lowercase     						=np.cumsum((dists >= 0)  , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
            __lowercase     						=low_idx + 1

            __lowercase     						=log_sigmas[low_idx]
            __lowercase     						=log_sigmas[high_idx]

            # interpolate sigmas
            __lowercase     						=(low - log_sigma) / (low - high)
            __lowercase     						=np.clip(__lowercase  , 0  , 1 )

            # transform interpolation to time range
            __lowercase     						=(1 - w) * low_idx + w * high_idx
            __lowercase     						=t.reshape(sigma.shape )
            return t







       def      snake_case				(							self     :	List[str]  , __lowercase     :	torch.FloatTensor  , __lowercase     :	Dict ):







            """simple docstring"""







            __lowercase     						=in_sigmas[-1].item()
            __lowercase     						=in_sigmas[0].item()

            __lowercase     						=7.0  # 7.0 is the value used in the paper
            __lowercase     						=np.linspace(0  , 1  , __lowercase )
            __lowercase     						=sigma_min ** (1 / rho)
            __lowercase     						=sigma_max ** (1 / rho)
            __lowercase     						=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
            return sigmas







       def      snake_case				(							self     :	Dict  , __lowercase     :	torch.FloatTensor  , __lowercase     :	Union[float, torch.FloatTensor]  , __lowercase     :	torch.FloatTensor  , __lowercase     :	float = 0.0  , __lowercase     :	float = 0.0  , __lowercase     :	float = float('inf' )  , __lowercase     :	float = 1.0  , __lowercase     :	Optional[torch.Generator] = None  , __lowercase     :	bool = True  , ):







            """simple docstring"""







            if (
                isinstance(__lowercase  , __lowercase )
                or isinstance(__lowercase  , torch.IntTensor )
                or isinstance(__lowercase  , torch.LongTensor )
            ):
                 raise ValueError(
                     (
                         'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                         ' `EulerDiscreteScheduler.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(__lowercase  , torch.Tensor ):
                 __lowercase     						=timestep.to(self.timesteps.device )

            __lowercase     						=(self.timesteps == timestep).nonzero().item()
            __lowercase     						=self.sigmas[step_index]

            __lowercase     						=min(s_churn / (len(self.sigmas ) - 1)  , 2**0.5 - 1 ) if s_tmin <= sigma <= s_tmax else 0.0

            __lowercase     						=randn_tensor(
                model_output.shape  , dtype=model_output.dtype  , device=model_output.device  , generator=__lowercase )

            __lowercase     						=noise * s_noise
            __lowercase     						=sigma * (gamma + 1)

            if gamma > 0:
                 __lowercase     						=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
            # NOTE: "original_sample" should not be an expected prediction_type but is left in for
            # backwards compatibility
            if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                 __lowercase     						=model_output
            elif self.config.prediction_type == "epsilon":
                 __lowercase     						=sample - sigma_hat * model_output
            elif self.config.prediction_type == "v_prediction":
                 # * c_out + input * c_skip
                 __lowercase     						=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
            else:
                 raise ValueError(
                     f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

            # 2. Convert to an ODE derivative
            __lowercase     						=(sample - pred_original_sample) / sigma_hat

            __lowercase     						=self.sigmas[step_index + 1] - sigma_hat

            __lowercase     						=sample + derivative * dt

            if not return_dict:
                 return (prev_sample,)

            return EulerDiscreteSchedulerOutput(prev_sample=__lowercase  , pred_original_sample=__lowercase )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	torch.FloatTensor  , __lowercase     :	torch.FloatTensor  , __lowercase     :	torch.FloatTensor  , ):







            """simple docstring"""







            __lowercase     						=self.sigmas.to(device=original_samples.device  , dtype=original_samples.dtype )
            if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase ):
                 # mps does not support float64
                 __lowercase     						=self.timesteps.to(original_samples.device  , dtype=torch.floataa )
                 __lowercase     						=timesteps.to(original_samples.device  , dtype=torch.floataa )
            else:
                 __lowercase     						=self.timesteps.to(original_samples.device )
                 __lowercase     						=timesteps.to(original_samples.device )

            __lowercase     						=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

            __lowercase     						=original_samples + noise * sigma
            return noisy_samples







       def __len__(							self     :	List[Any] ):







            """simple docstring"""







            return self.config.num_train_timesteps





'''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 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, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







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







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __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







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







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







       def      snake_case				(							self     :	str  , __lowercase     :	ImageInput  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Dict[str, int] = None  , __lowercase     :	PILImageResampling = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[float] = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[str, TensorType]] = None  , __lowercase     :	Union[str, ChannelDimension] = ChannelDimension.FIRST  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __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     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                 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_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.' )

            # All transformations expect numpy arrays.
            __lowercase     						=[to_numpy_array(__lowercase ) for image in images]

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

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

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

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

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


