





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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	42







class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	1




       @register_to_config
       def __init__(    self					:       Union[str, Any]				,							A					:       int = 40				,							A					:       float = 0.002				,							A					:       float = 80.0				,							A					:       float = 0.5				,							A					:       float = 1.0				,							A					:       float = 7.0				,							A					:       bool = True				,							):
         # standard deviation of the initial noise distribution
         __snake_case:		Tuple					    =		sigma_max

         __snake_case:		List[Any]					    =		np.linspace(0				,							1				,							A						)
         __snake_case:		Dict					    =		self._convert_to_karras(A						)
         __snake_case:		List[Any]					    =		self.sigma_to_t(A						)

         # setable values
         __snake_case:		Union[str, Any]					    =		None
         __snake_case:		Optional[int]					    =		torch.from_numpy(A						)
         __snake_case:		Optional[Any]					    =		torch.from_numpy(A						)
         __snake_case:		Optional[int]					    =		False
         __snake_case:		Union[str, Any]					    =		False




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

         __snake_case:		str					    =		(schedule_timesteps == timestep).nonzero()
         return indices.item()




       def 				UpperCAmelCase__					(    self					:       Tuple				,							A					:       torch.FloatTensor				,							A					:       Union[float, torch.FloatTensor]						):
         # Get sigma corresponding to timestep
         if isinstance(A				,							torch.Tensor						):
           __snake_case:		str					    =		timestep.to(self.timesteps.device						)
         __snake_case:		List[str]					    =		self.index_for_timestep(A						)
         __snake_case:		Dict					    =		self.sigmas[step_idx]

         __snake_case:		Optional[int]					    =		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         __snake_case:		List[str]					    =		True
         return sample




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Union[float, np.ndarray]						):
         if not isinstance(A				,							np.ndarray						):
           __snake_case:		Any					    =		np.array(A				,							dtype=np.floataa						)

         __snake_case:		List[Any]					    =		1_000 * 0.25 * np.log(sigmas + 1E-44						)

         return timesteps




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

           __snake_case:		Optional[int]					    =		np.array(A				,							dtype=np.intaa						)
           __snake_case:		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.'''						)

           __snake_case:		Optional[Any]					    =		num_inference_steps

           __snake_case:		Dict					    =		self.config.num_train_timesteps // self.num_inference_steps
           __snake_case:		int					    =		(np.arange(0				,							A						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
           __snake_case:		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
         __snake_case:		int					    =		self.config.num_train_timesteps
         __snake_case:		Optional[Any]					    =		timesteps[::-1].copy()
         __snake_case:		Union[str, Any]					    =		ramp / (num_train_timesteps - 1)
         __snake_case:		List[Any]					    =		self._convert_to_karras(A						)
         __snake_case:		Optional[int]					    =		self.sigma_to_t(A						)

         __snake_case:		Tuple					    =		np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
         __snake_case:		List[str]					    =		torch.from_numpy(A						).to(device=A						)

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




       def 				UpperCAmelCase__					(    self					:       Tuple				,							A					:       Dict						):
         __snake_case:		float					    =		self.config.sigma_min
         __snake_case:		float					    =		self.config.sigma_max

         __snake_case:		int					    =		self.config.rho
         __snake_case:		Any					    =		sigma_min ** (1 / rho)
         __snake_case:		Optional[Any]					    =		sigma_max ** (1 / rho)
         __snake_case:		Union[str, Any]					    =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       Union[str, Any]						):
         __snake_case:		int					    =		self.config.sigma_data

         __snake_case:		int					    =		sigma_data**2 / (sigma**2 + sigma_data**2)
         __snake_case:		Dict					    =		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       str						):
         __snake_case:		Any					    =		self.config.sigma_min
         __snake_case:		Tuple					    =		self.config.sigma_data

         __snake_case:		Union[str, Any]					    =		sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         __snake_case:		str					    =		(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out




       def 				UpperCAmelCase__					(    self					:       Optional[int]				,							A					:       torch.FloatTensor				,							A					:       Union[float, torch.FloatTensor]				,							A					:       torch.FloatTensor				,							A					:       Optional[torch.Generator] = None				,							A					:       bool = True				,							):

         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						):
           __snake_case:		int					    =		timestep.to(self.timesteps.device						)

         __snake_case:		int					    =		self.config.sigma_min
         __snake_case:		str					    =		self.config.sigma_max

         __snake_case:		Optional[int]					    =		self.index_for_timestep(A						)

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

         # Get scalings for boundary conditions
         __snake_case    ,  __snake_case:		str					    =		self.get_scalings_for_boundary_condition(A						)

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

         __snake_case:		List[Any]					    =		sigma_next.clamp(min=A				,							max=A						)

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

         if not return_dict:
           return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=A						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							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:		str					    =		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:		Optional[Any]					    =		self.timesteps.to(original_samples.device				,							dtype=torch.floataa						)
           __snake_case:		str					    =		timesteps.to(original_samples.device				,							dtype=torch.floataa						)
         else:
           __snake_case:		Union[str, Any]					    =		self.timesteps.to(original_samples.device						)
           __snake_case:		Optional[int]					    =		timesteps.to(original_samples.device						)

         __snake_case:		List[str]					    =		[(schedule_timesteps == t).nonzero().item() 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:		Tuple					    =		original_samples + noise * sigma
         return noisy_samples






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













from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	1
       lowerCAmelCase__ 						=	2
       lowerCAmelCase__ 						=	3




       def __repr__(    self					:       Optional[int]						):
         return f'''{self.__class__.__name__}.{self.name}'''







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       float:
  __snake_case    ,  __snake_case:		Union[str, Any]					    =		point
  return degrees(atana(y - miny   ,   x - minx))







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Direction:
  __snake_case    ,  __snake_case:		Dict					    =		starting
  __snake_case    ,  __snake_case:		List[str]					    =		via
  __snake_case    ,  __snake_case:		Optional[int]					    =		target
  __snake_case:		int					    =		degrees(atana(ya - ya   ,   xa - xa))
  via_angle %= 360
  __snake_case:		List[Any]					    =		degrees(atana(ya - ya   ,   xa - xa))
  target_angle %= 360
  # t-
  #  \ \
  #   \ v
  #    \|
  #     s
  # via_angle is always lower than target_angle, if direction is left.
  # If they are same, it means they are on a same line of convex hull.
  if target_angle > via_angle:
    return Direction.left
  elif target_angle == via_angle:
    return Direction.straight
  else:
    return Direction.right







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       list[tuple[int, int]]:

  if len(SCREAMING_SNAKE_CASE__) <= 2:
    # There is no convex hull
    raise ValueError("""graham_scan: argument must contain more than 3 points.""")
  if len(SCREAMING_SNAKE_CASE__) == 3:
    return points
  # find the lowest and the most left point
  __snake_case:		Optional[int]					    =		0
  __snake_case    ,  __snake_case:		Optional[int]					    =		maxsize, maxsize
  for i, point in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:		Tuple					    =		point[0]
    __snake_case:		Optional[Any]					    =		point[1]
    if y < miny:
      __snake_case:		Optional[Any]					    =		y
      __snake_case:		Union[str, Any]					    =		x
      __snake_case:		Any					    =		i
    if y == miny and x < minx:
      __snake_case:		Optional[Any]					    =		x
      __snake_case:		str					    =		i

    # remove the lowest and the most left point from points for preparing for sort
  points.pop(SCREAMING_SNAKE_CASE__)

  __snake_case:		Optional[int]					    =		sorted(SCREAMING_SNAKE_CASE__   ,   key=lambda SCREAMING_SNAKE_CASE__: angle_comparer(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  # This insert actually costs complexity,
  # and you should instead add (minx, miny) into stack later.
  # I'm using insert just for easy understanding.
  sorted_points.insert(0   ,   (minx, miny))

  __snake_case:		deque[tuple[int, int]]					    =		deque()
  stack.append(sorted_points[0])
  stack.append(sorted_points[1])
  stack.append(sorted_points[2])
  # In any ways, the first 3 points line are towards left.
  # Because we sort them the angle from minx, miny.
  __snake_case:		Optional[Any]					    =		Direction.left

  for i in range(3   ,   len(SCREAMING_SNAKE_CASE__)):
    while True:
      __snake_case:		Optional[Any]					    =		stack[-2]
      __snake_case:		List[Any]					    =		stack[-1]
      __snake_case:		int					    =		sorted_points[i]
      __snake_case:		Union[str, Any]					    =		check_direction(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

      if next_direction == Direction.left:
        __snake_case:		Dict					    =		Direction.left
        break
      if next_direction == Direction.straight:
        if current_direction == Direction.left:
          # We keep current_direction as left.
          # Because if the straight line keeps as straight,
          # we want to know if this straight line is towards left.
          break
        elif current_direction == Direction.right:
          # If the straight line is towards right,
          # every previous points on those straigh line is not convex hull.
          stack.pop()
      if next_direction == Direction.right:
        stack.pop()
    stack.append(sorted_points[i])
  return list(SCREAMING_SNAKE_CASE__)






