
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 							A__		(     lowerCamelCase__):
      _UpperCAmelCase    :   torch.FloatTensor





class 							A__		(     lowerCamelCase__  ,       lowerCamelCase__):
      _UpperCAmelCase    :   Optional[Any]   	=      1
      @register_to_config
      def __init__(				self   ,  __magic_name__ = 4_0   ,  __magic_name__ = 0.002   ,  __magic_name__ = 80.0   ,  __magic_name__ = 0.5   ,  __magic_name__ = 1.0   ,  __magic_name__ = 7.0   ,  __magic_name__ = True   ,  ):
       lowerCamelCase		:     Any          =				sigma_max

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

       # setable values
       lowerCamelCase		:     str          =				None
       lowerCamelCase		:     Any          =				torch.from_numpy(lowercase_				)
       lowerCamelCase		:     Optional[int]          =				torch.from_numpy(lowercase_				)
       lowerCamelCase		:     Tuple          =				False
       lowerCamelCase		:     Union[str, Any]          =				False
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=None				):






       if schedule_timesteps is None:
        lowerCamelCase		:     Optional[int]          =				self.timesteps

       lowerCamelCase		:     Union[str, Any]          =				(schedule_timesteps == timestep).nonzero()
       return indices.item()
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):






       if isinstance(lowercase_   ,  torch.Tensor				):
        lowerCamelCase		:     Optional[Any]          =				timestep.to(self.timesteps.device				)
       lowerCamelCase		:     Tuple          =				self.index_for_timestep(lowercase_				)
       lowerCamelCase		:     Optional[Any]          =				self.sigmas[step_idx]

       lowerCamelCase		:     List[str]          =				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       lowerCamelCase		:     List[Any]          =				True
       return sample
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):






       if not isinstance(lowercase_   ,  np.ndarray				):
        lowerCamelCase		:     str          =				np.array(lowercase_   ,  dtype=np.floataa				)

       lowerCamelCase		:     Dict          =				1_0_0_0 * 0.25 * np.log(sigmas + 1e-44				)

       return timesteps
      def 						UpperCamelCase__							(				self   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  ):






       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(lowercase_				)				):
         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}.'''				)

        lowerCamelCase		:     Tuple          =				np.array(lowercase_   ,  dtype=np.intaa				)
        lowerCamelCase		:     Union[str, Any]          =				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.'''				)

        lowerCamelCase		:     Tuple          =				num_inference_steps

        lowerCamelCase		:     int          =				self.config.num_train_timesteps // self.num_inference_steps
        lowerCamelCase		:     Union[str, Any]          =				(np.arange(0   ,  lowercase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
        lowerCamelCase		:     Any          =				False

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

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

       if str(lowercase_				).startswith("""mps"""				):
        # mps does not support float64
        lowerCamelCase		:     Optional[Any]          =				torch.from_numpy(lowercase_				).to(lowercase_   ,  dtype=torch.floataa				)
       else:
        lowerCamelCase		:     Optional[Any]          =				torch.from_numpy(lowercase_				).to(device=lowercase_				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     str          =				self.config.sigma_min
       lowerCamelCase		:     Optional[Any]          =				self.config.sigma_max

       lowerCamelCase		:     Optional[int]          =				self.config.rho
       lowerCamelCase		:     List[Any]          =				sigma_min ** (1 / rho)
       lowerCamelCase		:     Tuple          =				sigma_max ** (1 / rho)
       lowerCamelCase		:     Union[str, Any]          =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     List[str]          =				self.config.sigma_data

       lowerCamelCase		:     List[Any]          =				sigma_data**2 / (sigma**2 + sigma_data**2)
       lowerCamelCase		:     Optional[Any]          =				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     Tuple          =				self.config.sigma_min
       lowerCamelCase		:     Union[str, Any]          =				self.config.sigma_data

       lowerCamelCase		:     List[str]          =				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       lowerCamelCase		:     Tuple          =				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = None   ,  __magic_name__ = True   ,  ):






       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"""
                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(lowercase_   ,  torch.Tensor				):
        lowerCamelCase		:     Optional[Any]          =				timestep.to(self.timesteps.device				)

       lowerCamelCase		:     Optional[Any]          =				self.config.sigma_min
       lowerCamelCase		:     Union[str, Any]          =				self.config.sigma_max

       lowerCamelCase		:     Optional[Any]          =				self.index_for_timestep(lowercase_				)

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

       # Get scalings for boundary conditions
       lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				self.get_scalings_for_boundary_condition(lowercase_				)

       # 1. Denoise model output using boundary conditions
       lowerCamelCase		:     Any          =				c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
        lowerCamelCase		:     Any          =				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:
        lowerCamelCase		:     Optional[int]          =				randn_tensor(
            model_output.shape   ,  dtype=model_output.dtype   ,  device=model_output.device   ,  generator=lowercase_				)
       else:
        lowerCamelCase		:     Any          =				torch.zeros_like(lowercase_				)
       lowerCamelCase		:     Optional[int]          =				noise * self.config.s_noise

       lowerCamelCase		:     Union[str, Any]          =				sigma_next.clamp(min=lowercase_   ,  max=lowercase_				)

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

       if not return_dict:
        return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  ):
       lowerCamelCase		:     List[str]          =				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
        lowerCamelCase		:     int          =				self.timesteps.to(original_samples.device   ,  dtype=torch.floataa				)
        lowerCamelCase		:     int          =				timesteps.to(original_samples.device   ,  dtype=torch.floataa				)
       else:
        lowerCamelCase		:     Union[str, Any]          =				self.timesteps.to(original_samples.device				)
        lowerCamelCase		:     int          =				timesteps.to(original_samples.device				)

       lowerCamelCase		:     Optional[Any]          =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       lowerCamelCase		:     int          =				original_samples + noise * sigma
       return noisy_samples






      def __len__(				self				):
       return self.config.num_train_timesteps






import sys





def    _a (						lowerCamelCase,     lowerCamelCase							):
 if not numbers:
  return 0
 if target == 0 and target in numbers:
  return 0
 if not isinstance(lowerCamelCase,     (list, tuple)							) or not all(
     isinstance(lowerCamelCase,     lowerCamelCase							) for number in numbers							):
  raise ValueError("""numbers must be an iterable of integers"""							)

 lowerCamelCase		:     str          =				0
 lowerCamelCase		:     List[str]          =				sys.maxsize

 while right < len(lowerCamelCase							):
  curr_sum += numbers[right]
  while curr_sum >= target and left <= right:
   lowerCamelCase		:     Any          =				min(lowerCamelCase,     right - left + 1							)
   curr_sum -= numbers[left]
   left += 1
  right += 1

 return 0 if min_len == sys.maxsize else min_len




