

'''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


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




@dataclass
class 			A_					(							_A				):






     '''simple docstring'''



     UpperCAmelCase_		:      Any    						=    42




class 			A_					(							_A			,  _A				):






     '''simple docstring'''



     UpperCAmelCase_		:      Optional[int]    						=    1


     @register_to_config
     def __init__(						self					:	Any	,     lowercase_					:	int = 40	,     lowercase_					:	float = 0.002	,     lowercase_					:	float = 80.0	,     lowercase_					:	float = 0.5	,     lowercase_					:	float = 1.0	,     lowercase_					:	float = 7.0	,     lowercase_					:	bool = True	,     )		->      Optional[Any]:
      UpperCAmelCase			:      Any		   =					sigma_max

      UpperCAmelCase			:      Optional[int]		   =					np.linspace(0	,     1	,     __A			)
      UpperCAmelCase			:      str		   =					self._convert_to_karras(__A			)
      UpperCAmelCase			:      Union[str, Any]		   =					self.sigma_to_t(__A			)

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


     def   UpperCAmelCase_			(						self					:	Optional[Any]	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Union[str, Any]=None			)		->      Optional[Any]:





      if schedule_timesteps is None:
       UpperCAmelCase			:      List[str]		   =					self.timesteps

      UpperCAmelCase			:      Union[str, Any]		   =					(schedule_timesteps == timestep).nonzero()
      return indices.item()


     def   UpperCAmelCase_			(						self					:	Any	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	Union[float, torch.FloatTensor]			)		->      torch.FloatTensor:





      if isinstance(__A	,     torch.Tensor			):
       UpperCAmelCase			:      Dict		   =					timestep.to(self.timesteps.device			)
      UpperCAmelCase			:      Any		   =					self.index_for_timestep(__A			)
      UpperCAmelCase			:      Dict		   =					self.sigmas[step_idx]

      UpperCAmelCase			:      int		   =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

      UpperCAmelCase			:      Union[str, Any]		   =					True
      return sample


     def   UpperCAmelCase_			(						self					:	List[str]	,     lowercase_					:	Union[float, np.ndarray]			)		->      Any:





      if not isinstance(__A	,     np.ndarray			):
       UpperCAmelCase			:      Union[str, Any]		   =					np.array(__A	,     dtype=np.floataa			)

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

      return timesteps


     def   UpperCAmelCase_			(						self					:	Optional[Any]	,     lowercase_					:	Optional[int] = None	,     lowercase_					:	Union[str, torch.device] = None	,     lowercase_					:	Optional[List[int]] = None	,     )		->      Optional[Any]:





      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}."""			)

       UpperCAmelCase			:      List[str]		   =					np.array(__A	,     dtype=np.intaa			)
       UpperCAmelCase			:      List[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."""			)

       UpperCAmelCase			:      List[str]		   =					num_inference_steps

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

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

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

      if str(__A			).startswith('mps'			):
       # mps does not support float64
       UpperCAmelCase			:      int		   =					torch.from_numpy(__A			).to(__A	,     dtype=torch.floataa			)
      else:
       UpperCAmelCase			:      Optional[int]		   =					torch.from_numpy(__A			).to(device=__A			)


     def   UpperCAmelCase_			(						self					:	Tuple	,     lowercase_					:	Union[str, Any]			)		->      List[Any]:
      UpperCAmelCase			:      Dict		   =					self.config.sigma_min
      UpperCAmelCase			:      Optional[Any]		   =					self.config.sigma_max

      UpperCAmelCase			:      List[Any]		   =					self.config.rho
      UpperCAmelCase			:      List[Any]		   =					sigma_min ** (1 / rho)
      UpperCAmelCase			:      int		   =					sigma_max ** (1 / rho)
      UpperCAmelCase			:      Any		   =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas


     def   UpperCAmelCase_			(						self					:	List[str]	,     lowercase_					:	List[Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.config.sigma_data

      UpperCAmelCase			:      Any		   =					sigma_data**2 / (sigma**2 + sigma_data**2)
      UpperCAmelCase			:      List[str]		   =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out


     def   UpperCAmelCase_			(						self					:	Optional[Any]	,     lowercase_					:	Tuple			)		->      Tuple:
      UpperCAmelCase			:      int		   =					self.config.sigma_min
      UpperCAmelCase			:      Dict		   =					self.config.sigma_data

      UpperCAmelCase			:      Optional[Any]		   =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
      UpperCAmelCase			:      Optional[int]		   =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out


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





      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			):
       UpperCAmelCase			:      Optional[int]		   =					timestep.to(self.timesteps.device			)

      UpperCAmelCase			:      Optional[int]		   =					self.config.sigma_min
      UpperCAmelCase			:      Optional[Any]		   =					self.config.sigma_max

      UpperCAmelCase			:      List[Any]		   =					self.index_for_timestep(__A			)

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

      # Get scalings for boundary conditions
      UpperCAmelCase   ,					UpperCAmelCase			:      str		   =					self.get_scalings_for_boundary_condition(__A			)

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

      UpperCAmelCase			:      int		   =					sigma_next.clamp(min=__A	,     max=__A			)

      # 3. Return noisy sample
      # tau = sigma_hat, eps = sigma_min
      UpperCAmelCase			:      List[str]		   =					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					:	Union[str, Any]	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	torch.FloatTensor	,     lowercase_					:	torch.FloatTensor	,     )		->      torch.FloatTensor:
      UpperCAmelCase			:      Dict		   =					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
       UpperCAmelCase			:      Dict		   =					self.timesteps.to(original_samples.device	,     dtype=torch.floataa			)
       UpperCAmelCase			:      Optional[int]		   =					timesteps.to(original_samples.device	,     dtype=torch.floataa			)
      else:
       UpperCAmelCase			:      str		   =					self.timesteps.to(original_samples.device			)
       UpperCAmelCase			:      Optional[int]		   =					timesteps.to(original_samples.device			)

      UpperCAmelCase			:      Tuple		   =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

      UpperCAmelCase			:      Any		   =					original_samples + noise * sigma
      return noisy_samples




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









'''simple docstring'''







_UpperCamelCase  						=			{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

_UpperCamelCase  						=			{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}
def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	str					, UpperCAmelCase_      :	float						) ->							float:
							if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
														lowerCAmelCase__  =   (
														    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
														    F'''Supported values are: {", ".join(UpperCAmelCase_						)}'''
														)
														raise ValueError(UpperCAmelCase_						)
							return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()





