





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=0.999					,					SCREAMING_SNAKE_CASE__="cosine"					,					)   ->    Union[str, Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE__):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE__):
      return math.exp(t * -12.0)

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

  __snake_case:						List[str]   =						[]
  for i in range(SCREAMING_SNAKE_CASE__):
    __snake_case:						List[str]   =						i / num_diffusion_timesteps
    __snake_case:						Tuple   =						(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__) / alpha_bar_fn(SCREAMING_SNAKE_CASE__)					,					SCREAMING_SNAKE_CASE__))
  return torch.tensor(SCREAMING_SNAKE_CASE__					,					dtype=torch.floataa)







class     __snake_case       (							__lowerCamelCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [e.name for e in KarrasDiffusionSchedulers]
       lowerCAmelCase__      		=     2




       @register_to_config
       def __init__(  self :    List[str]							,				A :    int = 1_000							,				A :    float = 0.0_0085							,				A :    float = 0.012							,				A :    str = "linear"							,				A :    Optional[Union[np.ndarray, List[float]]] = None							,				A :    str = "epsilon"							,				A :    str = "linspace"							,				A :    int = 0							,				):
         if trained_betas is not None:
           __snake_case:						List[str]   =						torch.tensor(A							,				dtype=torch.floataa		)
         elif beta_schedule == "linear":
           __snake_case:						Optional[Any]   =						torch.linspace(A							,				A							,				A							,				dtype=torch.floataa		)
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           __snake_case:						Optional[int]   =						(
               torch.linspace(beta_start**0.5							,				beta_end**0.5							,				A							,				dtype=torch.floataa		) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           __snake_case:						Optional[int]   =						betas_for_alpha_bar(A		)
         else:
           raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''		)

         __snake_case:						List[Any]   =						1.0 - self.betas
         __snake_case:						List[Any]   =						torch.cumprod(self.alphas							,				dim=0		)

         #  set all values
         self.set_timesteps(A							,				A							,				A		)




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    List[str]							,				A :    List[str]=None		):
         if schedule_timesteps is None:
           __snake_case:						Union[str, Any]   =						self.timesteps

         __snake_case:						Any   =						(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter		) == 0:
           __snake_case:						Dict   =						1 if len(A		) > 1 else 0
         else:
           __snake_case:						Dict   =						timestep.cpu().item() if torch.is_tensor(A		) else timestep
           __snake_case:						Any   =						self._index_counter[timestep_int]

         return indices[pos].item()




       @property
       def 							UpperCAmelCase__ (  self :    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 							UpperCAmelCase__ (  self :    Dict							,				A :    torch.FloatTensor							,				A :    Union[float, torch.FloatTensor]							,				):
         __snake_case:						str   =						self.index_for_timestep(A		)

         if self.state_in_first_order:
           __snake_case:						str   =						self.sigmas[step_index]
         else:
           __snake_case:						Union[str, Any]   =						self.sigmas_interpol[step_index]

         __snake_case:						Tuple   =						sample / ((sigma**2 + 1) ** 0.5)
         return sample




       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    int							,				A :    Union[str, torch.device] = None							,				A :    Optional[int] = None							,				):
         __snake_case:						Tuple   =						num_inference_steps

         __snake_case:						Any   =						num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
           __snake_case:						Optional[Any]   =						np.linspace(0							,				num_train_timesteps - 1							,				A							,				dtype=A		)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
           __snake_case:						List[Any]   =						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
           __snake_case:						List[str]   =						(np.arange(0							,				A		) * step_ratio).round()[::-1].copy().astype(A		)
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           __snake_case:						Union[str, Any]   =						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
           __snake_case:						Optional[int]   =						(np.arange(A							,				0							,				-step_ratio		)).round().copy().astype(A		)
           timesteps -= 1
         else:
           raise ValueError(
               f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

         __snake_case:						Optional[Any]   =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
         __snake_case:						int   =						torch.from_numpy(np.log(A		)		).to(A		)

         __snake_case:						Tuple   =						np.interp(A							,				np.arange(0							,				len(A		)		)							,				A		)
         __snake_case:						Union[str, Any]   =						np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
         __snake_case:						Any   =						torch.from_numpy(A		).to(device=A		)

         # interpolate sigmas
         __snake_case:						List[Any]   =						sigmas.log().lerp(sigmas.roll(1		).log()							,				0.5		).exp()

         __snake_case:						List[str]   =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
         __snake_case:						str   =						torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

         if str(A		).startswith("""mps"""		):
           # mps does not support float64
           __snake_case:						Any   =						torch.from_numpy(A		).to(A							,				dtype=torch.floataa		)
         else:
           __snake_case:						Union[str, Any]   =						torch.from_numpy(A		).to(A		)

         # interpolate timesteps
         __snake_case:						List[str]   =						self.sigma_to_t(A		).to(A							,				dtype=timesteps.dtype		)
         __snake_case:						int   =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])							,				dim=-1		).flatten()

         __snake_case:						List[Any]   =						torch.cat([timesteps[:1], interleaved_timesteps]		)

         __snake_case:						Union[str, Any]   =						None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         __snake_case:						List[str]   =						defaultdict(A		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    List[str]		):
         # get log sigma
         __snake_case:						Optional[int]   =						sigma.log()

         # get distribution
         __snake_case:						List[str]   =						log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         __snake_case:						Any   =						dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
         __snake_case:						Any   =						low_idx + 1

         __snake_case:						List[str]   =						self.log_sigmas[low_idx]
         __snake_case:						Optional[int]   =						self.log_sigmas[high_idx]

         # interpolate sigmas
         __snake_case:						List[str]   =						(low - log_sigma) / (low - high)
         __snake_case:						str   =						w.clamp(0							,				1		)

         # transform interpolation to time range
         __snake_case:						Optional[Any]   =						(1 - w) * low_idx + w * high_idx
         __snake_case:						str   =						t.view(sigma.shape		)
         return t




       @property
       def 							UpperCAmelCase__ (  self :    List[str]		):
         return self.sample is None




       def 							UpperCAmelCase__ (  self :    Any							,				A :    Union[torch.FloatTensor, np.ndarray]							,				A :    Union[float, torch.FloatTensor]							,				A :    Union[torch.FloatTensor, np.ndarray]							,				A :    bool = True							,				):
         __snake_case:						List[str]   =						self.index_for_timestep(A		)

         # advance index counter by 1
         __snake_case:						List[Any]   =						timestep.cpu().item() if torch.is_tensor(A		) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
           __snake_case:						Any   =						self.sigmas[step_index]
           __snake_case:						Optional[Any]   =						self.sigmas_interpol[step_index + 1]
           __snake_case:						List[Any]   =						self.sigmas[step_index + 1]
         else:
           # 2nd order / KDPM2's method
           __snake_case:						Union[str, Any]   =						self.sigmas[step_index - 1]
           __snake_case:						List[str]   =						self.sigmas_interpol[step_index]
           __snake_case:						List[str]   =						self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         __snake_case:						Optional[Any]   =						0
         __snake_case:						List[Any]   =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
           __snake_case:						Tuple   =						sigma_hat if self.state_in_first_order else sigma_interpol
           __snake_case:						Dict   =						sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
           __snake_case:						Union[str, Any]   =						sigma_hat if self.state_in_first_order else sigma_interpol
           __snake_case:						int   =						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
         elif self.config.prediction_type == "sample":
           raise NotImplementedError("""prediction_type not implemented yet: sample"""		)
         else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''		)

         if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           __snake_case:						Tuple   =						(sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           __snake_case:						int   =						sigma_interpol - sigma_hat

           # store for 2nd order step
           __snake_case:						List[str]   =						sample
         else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           __snake_case:						Tuple   =						(sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           __snake_case:						Tuple   =						sigma_next - sigma_hat

           __snake_case:						Any   =						self.sample
           __snake_case:						Union[str, Any]   =						None

         __snake_case:						List[str]   =						sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=A		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    torch.FloatTensor							,				A :    torch.FloatTensor							,				A :    torch.FloatTensor							,				):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         __snake_case:						Optional[Any]   =						self.sigmas.to(device=original_samples.device							,				dtype=original_samples.dtype		)
         if original_samples.device.type == "mps" and torch.is_floating_point(A		):
           # mps does not support float64
           __snake_case:						Any   =						self.timesteps.to(original_samples.device							,				dtype=torch.floataa		)
           __snake_case:						int   =						timesteps.to(original_samples.device							,				dtype=torch.floataa		)
         else:
           __snake_case:						int   =						self.timesteps.to(original_samples.device		)
           __snake_case:						List[Any]   =						timesteps.to(original_samples.device		)

         __snake_case:						int   =						[self.index_for_timestep(A							,				A		) for t in timesteps]

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

         __snake_case:						Union[str, Any]   =						original_samples + noise * sigma
         return noisy_samples






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













import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__UpperCAmelCase       :   Tuple				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				*A :    Union[str, Any]							,				**A :    Optional[int]		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,				A							,				)
         super().__init__(*A							,				**A		)






