
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


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





@dataclass
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							42





class 						__lowercase(     SCREAMING_SNAKE_CASE		,  SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							1



       @register_to_config
       def __init__(     self					:			Tuple  ,      _lowerCAmelCase					:			int = 40  ,      _lowerCAmelCase					:			float = 0.002  ,      _lowerCAmelCase					:			float = 80.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 7.0  ,      _lowerCAmelCase					:			bool = True  ,      )					->   Dict:
         # standard deviation of the initial noise distribution
         _lowerCAmelCase						=		sigma_max

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

         # setable values
         _lowerCAmelCase						=		None
         _lowerCAmelCase						=		torch.from_numpy(_lowerCAmelCase       )
         _lowerCAmelCase						=		torch.from_numpy(_lowerCAmelCase       )
         _lowerCAmelCase						=		False
         _lowerCAmelCase						=		False



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			int=None       )					->   str:
         if schedule_timesteps is None:
           _lowerCAmelCase						=		self.timesteps

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			torch.FloatTensor  ,      _lowerCAmelCase					:			Union[float, torch.FloatTensor]       )					->   torch.FloatTensor:
         # Get sigma corresponding to timestep
         if isinstance(_lowerCAmelCase  ,      torch.Tensor       ):
           _lowerCAmelCase						=		timestep.to(self.timesteps.device       )
         _lowerCAmelCase						=		self.index_for_timestep(_lowerCAmelCase       )
         _lowerCAmelCase						=		self.sigmas[step_idx]

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

         _lowerCAmelCase						=		True
         return sample



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Union[float, np.ndarray]       )					->   Any:
         if not isinstance(_lowerCAmelCase  ,      np.ndarray       ):
           _lowerCAmelCase						=		np.array(_lowerCAmelCase  ,      dtype=np.floataa       )

         _lowerCAmelCase						=		1000 * 0.25 * np.log(sigmas + 1e-44       )

         return timesteps



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[int] = None  ,      _lowerCAmelCase					:			Union[str, torch.device] = None  ,      _lowerCAmelCase					:			Optional[List[int]] = None  ,      )					->   str:
         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(_lowerCAmelCase       )       ):
             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						=		np.array(_lowerCAmelCase  ,      dtype=np.intaa       )
           _lowerCAmelCase						=		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						=		num_inference_steps

           _lowerCAmelCase						=		self.config.num_train_timesteps // self.num_inference_steps
           _lowerCAmelCase						=		(np.arange(0  ,      _lowerCAmelCase       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
           _lowerCAmelCase						=		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						=		self.config.num_train_timesteps
         _lowerCAmelCase						=		timesteps[::-1].copy()
         _lowerCAmelCase						=		ramp / (num_train_timesteps - 1)
         _lowerCAmelCase						=		self._convert_to_karras(_lowerCAmelCase       )
         _lowerCAmelCase						=		self.sigma_to_t(_lowerCAmelCase       )

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

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



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			Dict       )					->   Optional[Any]:
         _lowerCAmelCase						=		self.config.sigma_min
         _lowerCAmelCase						=		self.config.sigma_max

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[int]       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.config.sigma_data

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			str       )					->   Optional[Any]:
         _lowerCAmelCase						=		self.config.sigma_min
         _lowerCAmelCase						=		self.config.sigma_data

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			torch.FloatTensor  ,      _lowerCAmelCase					:			Union[float, torch.FloatTensor]  ,      _lowerCAmelCase					:			torch.FloatTensor  ,      _lowerCAmelCase					:			Optional[torch.Generator] = None  ,      _lowerCAmelCase					:			bool = True  ,      )					->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:

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

         _lowerCAmelCase						=		self.config.sigma_min
         _lowerCAmelCase						=		self.config.sigma_max

         _lowerCAmelCase						=		self.index_for_timestep(_lowerCAmelCase       )

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

         # Get scalings for boundary conditions
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.get_scalings_for_boundary_condition(_lowerCAmelCase       )

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

         _lowerCAmelCase						=		sigma_next.clamp(min=_lowerCAmelCase  ,      max=_lowerCAmelCase       )

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

         if not return_dict:
           return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCAmelCase       )



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

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

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

         _lowerCAmelCase						=		original_samples + noise * sigma
         return noisy_samples






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


from __future__ import annotations



def 	_a  (					__SCREAMING_SNAKE_CASE					:  list		,				__SCREAMING_SNAKE_CASE					:  list    ):






  """simple docstring"""




  _lowerCAmelCase						=		[None] * (len(__SCREAMING_SNAKE_CASE    ) + len(__SCREAMING_SNAKE_CASE    ))

  _lowerCAmelCase						=		0  # pointer to current index for left Half
  _lowerCAmelCase						=		0  # pointer to current index for the right Half
  _lowerCAmelCase						=		0  # pointer to current index for the sorted array Half

  while pointera < len(__SCREAMING_SNAKE_CASE    ) and pointera < len(__SCREAMING_SNAKE_CASE    ):
    if left_half[pointera] < right_half[pointera]:
      _lowerCAmelCase						=		left_half[pointera]
      pointera += 1
      index += 1
    else:
      _lowerCAmelCase						=		right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(__SCREAMING_SNAKE_CASE    ):
    _lowerCAmelCase						=		left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(__SCREAMING_SNAKE_CASE    ):
    _lowerCAmelCase						=		right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array



def 	_a  (					__SCREAMING_SNAKE_CASE					:  list    ):






  """simple docstring"""




  if len(__SCREAMING_SNAKE_CASE    ) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  _lowerCAmelCase						=		0 + (len(__SCREAMING_SNAKE_CASE    ) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  _lowerCAmelCase						=		array[:middle]
  _lowerCAmelCase						=		array[middle:]

  return merge(merge_sort(__SCREAMING_SNAKE_CASE    )		,				merge_sort(__SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
