




import math
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 KarrasDiffusionSchedulers, SchedulerMixin


__magic_name__     :     Union[str, Any]  							=					logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			torch.FloatTensor
     lowercase_   :			Optional[torch.FloatTensor]   =   None



def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase=0.999      ,   __lowerCAmelCase="cosine"      ,   ):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__lowerCAmelCase							):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(__lowerCAmelCase							):
      return math.exp(t * -12.0							)

  else:
    raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

  lowerCAmelCase__								=			[]
  for i in range(__lowerCAmelCase							):
    lowerCAmelCase__								=			i / num_diffusion_timesteps
    lowerCAmelCase__								=			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase							) / alpha_bar_fn(__lowerCAmelCase							)      ,   __lowerCAmelCase							)							)
  return torch.tensor(__lowerCAmelCase      ,   dtype=torch.floataa							)





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			Tuple   =   [e.name for e in KarrasDiffusionSchedulers]
     lowercase_   :			List[str]   =   1
     @register_to_config
     def __init__(	self  :		Union[str, Any]					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[Union[np.ndarray, List[float]]] = None					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		str = "linspace"					,	__lowerCamelCase  :		int = 0					,	):



       """simple docstring"""
       if trained_betas is not None:
         lowerCAmelCase__								=			torch.tensor(__lowerCamelCase					,	dtype=torch.floataa	)
       elif beta_schedule == "linear":
         lowerCAmelCase__								=			torch.linspace(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	dtype=torch.floataa	)
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         lowerCAmelCase__								=			(
             torch.linspace(beta_start**0.5					,	beta_end**0.5					,	__lowerCamelCase					,	dtype=torch.floataa	) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         lowerCAmelCase__								=			betas_for_alpha_bar(__lowerCamelCase	)
       else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""	)

       lowerCAmelCase__								=			1.0 - self.betas
       lowerCAmelCase__								=			torch.cumprod(self.alphas					,	dim=0	)

       lowerCAmelCase__								=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
       lowerCAmelCase__								=			np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	)

       # setable values
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			np.linspace(0					,	num_train_timesteps - 1					,	__lowerCamelCase					,	dtype=__lowerCamelCase	)[::-1].copy()
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	)
       lowerCAmelCase__								=			False
     @property
     def 					A__			(	self  :		Any	):



       """simple docstring"""
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5
     def 					A__			(	self  :		int					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Union[float, torch.FloatTensor]	):



       """simple docstring"""
       if isinstance(__lowerCamelCase					,	torch.Tensor	):
         lowerCAmelCase__								=			timestep.to(self.timesteps.device	)
       lowerCAmelCase__								=			(self.timesteps == timestep).nonzero().item()
       lowerCAmelCase__								=			self.sigmas[step_index]
       lowerCAmelCase__								=			sample / ((sigma**2 + 1) ** 0.5)
       lowerCAmelCase__								=			True
       return sample
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Union[str, torch.device] = None	):



       """simple docstring"""
       lowerCAmelCase__								=			num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
         lowerCAmelCase__								=			np.linspace(0					,	self.config.num_train_timesteps - 1					,	__lowerCamelCase					,	dtype=__lowerCamelCase	)[
             ::-1
         ].copy()
       elif self.config.timestep_spacing == "leading":
         lowerCAmelCase__								=			self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         lowerCAmelCase__								=			(np.arange(0					,	__lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase	)
         timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
         lowerCAmelCase__								=			self.config.num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         lowerCAmelCase__								=			(np.arange(self.config.num_train_timesteps					,	0					,	-step_ratio	)).round().copy().astype(__lowerCamelCase	)
         timesteps -= 1
       else:
         raise ValueError(
             F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

       lowerCAmelCase__								=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
       lowerCAmelCase__								=			np.interp(__lowerCamelCase					,	np.arange(0					,	len(__lowerCamelCase	)	)					,	__lowerCamelCase	)
       lowerCAmelCase__								=			np.concatenate([sigmas, [0.0]]	).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 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Union[float, torch.FloatTensor]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Optional[torch.Generator] = None					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""

       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'''
                 ''' `EulerDiscreteScheduler.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.timesteps == timestep).nonzero().item()
       lowerCAmelCase__								=			self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         lowerCAmelCase__								=			sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         lowerCAmelCase__								=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
         raise NotImplementedError('''prediction_type not implemented yet: sample'''	)
       else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

       lowerCAmelCase__								=			self.sigmas[step_index]
       lowerCAmelCase__								=			self.sigmas[step_index + 1]
       lowerCAmelCase__								=			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
       lowerCAmelCase__								=			(sigma_to**2 - sigma_up**2) ** 0.5

       # 2. Convert to an ODE derivative
       lowerCAmelCase__								=			(sample - pred_original_sample) / sigma

       lowerCAmelCase__								=			sigma_down - sigma

       lowerCAmelCase__								=			sample + derivative * dt

       lowerCAmelCase__								=			model_output.device
       lowerCAmelCase__								=			randn_tensor(model_output.shape					,	dtype=model_output.dtype					,	device=__lowerCamelCase					,	generator=__lowerCamelCase	)

       lowerCAmelCase__								=			prev_sample + noise * sigma_up

       if not return_dict:
         return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=__lowerCamelCase					,	pred_original_sample=__lowerCamelCase	)
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		torch.FloatTensor					,	):



       """simple docstring"""
       # 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  :		Tuple	):



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







__magic_name__     :     dict[str, float]  							=					{
    "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": 50_00,
    "atomic-mass-unit": 6.022_136_652E26,
}

__magic_name__     :     dict[str, float]  							=					{
    "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": 1.660_540_199E-27,
}
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase							):
  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(__lowerCAmelCase							)}"""
    )
    raise ValueError(__lowerCAmelCase							)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

