






"""simple docstring"""




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


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







@dataclass
class 					_UpperCamelCase		( lowercase__):
    __lowerCamelCase         =  42







class 					_UpperCamelCase		( lowercase__						, lowercase__):
    __lowerCamelCase         =  1






    @register_to_config
    def __init__(self						,      lowerCamelCase__ = 4_0						,      lowerCamelCase__ = 0.0_0_2						,      lowerCamelCase__ = 8_0.0						,      lowerCamelCase__ = 0.5						,      lowerCamelCase__ = 1.0						,      lowerCamelCase__ = 7.0						,      lowerCamelCase__ = True						,      ):


      """simple docstring"""
      # standard deviation of the initial noise distribution
      A__											=							sigma_max

      A__											=							np.linspace(0						,      1						,      _A   )
      A__											=							self._convert_to_karras(_A   )
      A__											=							self.sigma_to_t(_A   )

      # setable values
      A__											=							None
      A__											=							torch.from_numpy(_A   )
      A__											=							torch.from_numpy(_A   )
      A__											=							False
      A__											=							False






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      if schedule_timesteps is None:
        A__											=							self.timesteps

      A__											=							(schedule_timesteps == timestep).nonzero()
      return indices.item()






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      # Get sigma corresponding to timestep
      if isinstance(_A						,      torch.Tensor   ):
        A__											=							timestep.to(self.timesteps.device   )
      A__											=							self.index_for_timestep(_A   )
      A__											=							self.sigmas[step_idx]

      A__											=							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

      A__											=							True
      return sample






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      if not isinstance(_A						,      np.ndarray   ):
        A__											=							np.array(_A						,      dtype=np.floataa   )

      A__											=							1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44   )

      return timesteps






    def  A    (self						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      ):


      """simple docstring"""
      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(_A   )   ):
          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}."""   )

        A__											=							np.array(_A						,      dtype=np.intaa   )
        A__											=							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."""   )

        A__											=							num_inference_steps

        A__											=							self.config.num_train_timesteps // self.num_inference_steps
        A__											=							(np.arange(0						,      _A   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
        A__											=							False

      # Map timesteps to Karras sigmas directly for multistep sampling
      # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
      A__											=							self.config.num_train_timesteps
      A__											=							timesteps[::-1].copy()
      A__											=							ramp / (num_train_timesteps - 1)
      A__											=							self._convert_to_karras(_A   )
      A__											=							self.sigma_to_t(_A   )

      A__											=							np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
      A__											=							torch.from_numpy(_A   ).to(device=_A   )

      if str(_A   ).startswith("""mps"""   ):
        # mps does not support float64
        A__											=							torch.from_numpy(_A   ).to(_A						,      dtype=torch.floataa   )
      else:
        A__											=							torch.from_numpy(_A   ).to(device=_A   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.config.sigma_min
      A__											=							self.config.sigma_max

      A__											=							self.config.rho
      A__											=							sigma_min ** (1 / rho)
      A__											=							sigma_max ** (1 / rho)
      A__											=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.config.sigma_data

      A__											=							sigma_data**2 / (sigma**2 + sigma_data**2)
      A__											=							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.config.sigma_min
      A__											=							self.config.sigma_data

      A__											=							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
      A__											=							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = True						,      ):


      """simple docstring"""

      if (
          isinstance(_A						,      _A   )
          or isinstance(_A						,      torch.IntTensor   )
          or isinstance(_A						,      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(_A						,      torch.Tensor   ):
        A__											=							timestep.to(self.timesteps.device   )

      A__											=							self.config.sigma_min
      A__											=							self.config.sigma_max

      A__											=							self.index_for_timestep(_A   )

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

      # Get scalings for boundary conditions
      A__											=							self.get_scalings_for_boundary_condition(_A   )

      # 1. Denoise model output using boundary conditions
      A__											=							c_out * model_output + c_skip * sample
      if self.config.clip_denoised:
        A__											=							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:
        A__											=							randn_tensor(
            model_output.shape						,      dtype=model_output.dtype						,      device=model_output.device						,      generator=_A   )
      else:
        A__											=							torch.zeros_like(_A   )
      A__											=							noise * self.config.s_noise

      A__											=							sigma_next.clamp(min=_A						,      max=_A   )

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

      if not return_dict:
        return (prev_sample,)

      return CMStochasticIterativeSchedulerOutput(prev_sample=_A   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


      """simple docstring"""
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      A__											=							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
        A__											=							self.timesteps.to(original_samples.device						,      dtype=torch.floataa   )
        A__											=							timesteps.to(original_samples.device						,      dtype=torch.floataa   )
      else:
        A__											=							self.timesteps.to(original_samples.device   )
        A__											=							timesteps.to(original_samples.device   )

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

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

      A__											=							original_samples + noise * sigma
      return noisy_samples






    def __len__(self   ):


      """simple docstring"""
      return self.config.num_train_timesteps












"""simple docstring"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime







class 					_UpperCamelCase		( __snake_case):






    def __init__(self						,      *lowerCamelCase__						,      **lowerCamelCase__   ):


      """simple docstring"""
      super().__init__(*lowerCamelCase__						,      **lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							(
          next_prime(value % self.size_table   )
          if not is_prime(value % self.size_table   )
          else value % self.size_table
      )  # gt = bigger than
      return next_prime_gt - (data % next_prime_gt)






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      return (increment * self.__hash_function_a(lowerCamelCase__						,      lowerCamelCase__   )) % self.size_table






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      A__											=							1
      A__											=							self.hash_function(lowerCamelCase__   )

      while self.values[new_key] is not None and self.values[new_key] != key:
        A__											=							(
            self.__hash_double_function(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   )
            if self.balanced_factor() >= self.lim_charge
            else None
        )
        if new_key is None:
          break
        else:
          i += 1

      return new_key




