





'''simple docstring'''







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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      torch.FloatTensor
      lowerCAmelCase__       :      Optional[torch.FloatTensor] 		=		None

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	str=0.9_9_9       ,  lowerCAmelCase__     :	str="cosine"       ,  )			->						Dict:

     '''simple docstring'''




     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase__     :	Optional[Any]):
               return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase__     :	Tuple):
               return math.exp(t * -1_2.0)

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

     _lowercase							:      Union[str, Any]  						=							[]
     for i in range(lowerCAmelCase__):
          _lowercase							:      Union[str, Any]  						=							i / num_diffusion_timesteps
          _lowercase							:      Optional[Any]  						=							(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__) / alpha_bar_fn(lowerCAmelCase__)       ,  lowerCAmelCase__))
     return torch.tensor(lowerCAmelCase__       ,  dtype=torch.floataa)



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Tuple 		=		[e.name for e in KarrasDiffusionSchedulers]
      lowerCAmelCase__       :      Tuple 		=		1

      @register_to_config
      def __init__(							self    : Dict   ,UpperCamelCase    : int = 1000   ,UpperCamelCase    : float = 0.0_0_0_1   ,UpperCamelCase    : float = 0.0_2   ,UpperCamelCase    : str = "linear"   ,UpperCamelCase    : Optional[Union[np.ndarray, List[float]]] = None   ,UpperCamelCase    : Optional[bool] = False   ,UpperCamelCase    : str = "epsilon"   ,UpperCamelCase    : str = "linspace"   ,UpperCamelCase    : int = 0   ,)		->  List[str]:
           if trained_betas is not None:
                _lowercase							:      str  						=							torch.tensor(UpperCamelCase   ,dtype=torch.floataa  )
           elif beta_schedule == "linear":
                _lowercase							:      List[Any]  						=							torch.linspace(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,dtype=torch.floataa  )
           elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                _lowercase							:      List[str]  						=							(
                    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							:      int  						=							betas_for_alpha_bar(UpperCamelCase  )
           else:
                raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

           _lowercase							:      Union[str, Any]  						=							1.0 - self.betas
           _lowercase							:      Tuple  						=							torch.cumprod(self.alphas   ,dim=0  )

           _lowercase							:      Dict  						=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
           _lowercase							:      Union[str, Any]  						=							np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
           _lowercase							:      Optional[Any]  						=							torch.from_numpy(UpperCamelCase  )

           # setable values
           _lowercase							:      Optional[int]  						=							None
           _lowercase							:      Union[str, Any]  						=							use_karras_sigmas
           self.set_timesteps(UpperCamelCase   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							[]
           _lowercase							:      Optional[int]  						=							False

      @property
      def   _lowerCamelCase		(							self    : List[str]  )		->  int:
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

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

      def   _lowerCamelCase		(							self    : List[Any]   ,UpperCamelCase    : torch.FloatTensor   ,UpperCamelCase    : Union[float, torch.FloatTensor]  )		->  torch.FloatTensor:
           if isinstance(UpperCamelCase   ,torch.Tensor  ):
                _lowercase							:      Optional[Any]  						=							timestep.to(self.timesteps.device  )
           _lowercase							:      Optional[int]  						=							(self.timesteps == timestep).nonzero().item()
           _lowercase							:      Optional[int]  						=							self.sigmas[step_index]
           _lowercase							:      Optional[int]  						=							sample / ((sigma**2 + 1) ** 0.5)
           _lowercase							:      int  						=							True
           return sample

      def   _lowerCamelCase		(							self    : Optional[int]   ,UpperCamelCase    : Tuple   ,UpperCamelCase    : Union[str, Any]   ,UpperCamelCase    : Any  )		->  Optional[int]:

           def lms_derivative(UpperCamelCase    : str  ):
                _lowercase							:      List[Any]  						=							1.0
                for k in range(UpperCamelCase  ):
                     if current_order == k:
                          continue
                     prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
                return prod

           _lowercase							:      int  						=							integrate.quad(UpperCamelCase   ,self.sigmas[t]   ,self.sigmas[t + 1]   ,epsrel=1e-4  )[0]

           return integrated_coeff

      def   _lowerCamelCase		(							self    : Optional[int]   ,UpperCamelCase    : int   ,UpperCamelCase    : Union[str, torch.device] = None  )		->  Any:
           _lowercase							:      Optional[Any]  						=							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							:      Any  						=							np.linspace(0   ,self.config.num_train_timesteps - 1   ,UpperCamelCase   ,dtype=UpperCamelCase  )[
                    ::-1
                ].copy()
           elif self.config.timestep_spacing == "leading":
                _lowercase							:      Optional[Any]  						=							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							:      Dict  						=							(np.arange(0   ,UpperCamelCase  ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase  )
                timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                _lowercase							:      Tuple  						=							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							:      Optional[Any]  						=							(np.arange(self.config.num_train_timesteps   ,0   ,-step_ratio  )).round().copy().astype(UpperCamelCase  )
                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							:      List[Any]  						=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
           _lowercase							:      Tuple  						=							np.log(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							np.interp(UpperCamelCase   ,np.arange(0   ,len(UpperCamelCase  )  )   ,UpperCamelCase  )

           if self.use_karras_sigmas:
                _lowercase							:      Dict  						=							self._convert_to_karras(in_sigmas=UpperCamelCase  )
                _lowercase							:      int  						=							np.array([self._sigma_to_t(UpperCamelCase   ,UpperCamelCase  ) for sigma in sigmas]  )

           _lowercase							:      Any  						=							np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )

           _lowercase							:      Tuple  						=							torch.from_numpy(UpperCamelCase  ).to(device=UpperCamelCase  )
           if str(UpperCamelCase  ).startswith('mps'  ):
                # mps does not support float64
                _lowercase							:      List[str]  						=							torch.from_numpy(UpperCamelCase  ).to(UpperCamelCase   ,dtype=torch.floataa  )
           else:
                _lowercase							:      List[str]  						=							torch.from_numpy(UpperCamelCase  ).to(device=UpperCamelCase  )

           _lowercase							:      Dict  						=							[]

      def   _lowerCamelCase		(							self    : Optional[Any]   ,UpperCamelCase    : int   ,UpperCamelCase    : str  )		->  List[str]:
           # get log sigma
           _lowercase							:      int  						=							np.log(UpperCamelCase  )

           # get distribution
           _lowercase							:      List[Any]  						=							log_sigma - log_sigmas[:, np.newaxis]

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

           _lowercase							:      Optional[Any]  						=							log_sigmas[low_idx]
           _lowercase							:      Tuple  						=							log_sigmas[high_idx]

           # interpolate sigmas
           _lowercase							:      Union[str, Any]  						=							(low - log_sigma) / (low - high)
           _lowercase							:      List[Any]  						=							np.clip(UpperCamelCase   ,0   ,1  )

           # transform interpolation to time range
           _lowercase							:      Tuple  						=							(1 - w) * low_idx + w * high_idx
           _lowercase							:      Optional[int]  						=							t.reshape(sigma.shape  )
           return t

      def   _lowerCamelCase		(							self    : Any   ,UpperCamelCase    : torch.FloatTensor  )		->  torch.FloatTensor:
           _lowercase							:      float  						=							in_sigmas[-1].item()
           _lowercase							:      float  						=							in_sigmas[0].item()

           _lowercase							:      Union[str, Any]  						=							7.0  # 7.0 is the value used in the paper
           _lowercase							:      Optional[int]  						=							np.linspace(0   ,1   ,self.num_inference_steps  )
           _lowercase							:      Tuple  						=							sigma_min ** (1 / rho)
           _lowercase							:      Union[str, Any]  						=							sigma_max ** (1 / rho)
           _lowercase							:      Dict  						=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas

      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : torch.FloatTensor   ,UpperCamelCase    : Union[float, torch.FloatTensor]   ,UpperCamelCase    : torch.FloatTensor   ,UpperCamelCase    : int = 4   ,UpperCamelCase    : bool = True   ,)		->  Union[LMSDiscreteSchedulerOutput, Tuple]:
           if not self.is_scale_input_called:
                warnings.warn(
                    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                    'See `StableDiffusionPipeline` for a usage example.'  )

           if isinstance(UpperCamelCase   ,torch.Tensor  ):
                _lowercase							:      Union[str, Any]  						=							timestep.to(self.timesteps.device  )
           _lowercase							:      Union[str, Any]  						=							(self.timesteps == timestep).nonzero().item()
           _lowercase							:      Union[str, Any]  						=							self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                _lowercase							:      List[str]  						=							sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                _lowercase							:      Optional[int]  						=							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
                _lowercase							:      List[Any]  						=							model_output
           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							:      Union[str, Any]  						=							(sample - pred_original_sample) / sigma
           self.derivatives.append(UpperCamelCase  )
           if len(self.derivatives  ) > order:
                self.derivatives.pop(0  )

           # 3. Compute linear multistep coefficients
           _lowercase							:      Optional[int]  						=							min(step_index + 1   ,UpperCamelCase  )
           _lowercase							:      List[Any]  						=							[self.get_lms_coefficient(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  ) for curr_order in range(UpperCamelCase  )]

           # 4. Compute previous sample based on the derivatives path
           _lowercase							:      Optional[int]  						=							sample + sum(
               coeff * derivative for coeff, derivative in zip(UpperCamelCase   ,reversed(self.derivatives  )  )  )

           if not return_dict:
                return (prev_sample,)

           return LMSDiscreteSchedulerOutput(prev_sample=UpperCamelCase   ,pred_original_sample=UpperCamelCase  )

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

           _lowercase							:      Optional[int]  						=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

           _lowercase							:      List[Any]  						=							original_samples + noise * sigma
           return noisy_samples



      def __len__(							self    : Dict  )		->  Tuple:
           return self.config.num_train_timesteps






'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A						    =							logging.get_logger(__name__)



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Tuple   ,*UpperCamelCase    : str   ,**UpperCamelCase    : List[Any]  )		->  None:
           warnings.warn(
               'The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
               ' Please use MobileNetV2ImageProcessor instead.'   ,UpperCamelCase   ,)
           super().__init__(*UpperCamelCase   ,**UpperCamelCase  )