






'''simple docstring'''




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


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		42
   snake_case					       =		None





def       __lowercase		(    __lowercase     ,						__lowercase=0.999     ,						__lowercase="cosine"     ,						) ->		Tuple:

      '''simple docstring'''


      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

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


class      _UpperCAmelCase    (  snake_case_							,					snake_case_		):




   """simple docstring"""

   snake_case					       =		[e.name for e in KarrasDiffusionSchedulers]
   snake_case					       =		1

   @register_to_config
   def __init__(	self				:			Union[str, Any]   ,   __UpperCAmelCase				:			int = 1000   ,   __UpperCAmelCase				:			float = 0.0001   ,   __UpperCAmelCase				:			float = 0.02   ,   __UpperCAmelCase				:			str = "linear"   ,   __UpperCAmelCase				:			Optional[Union[np.ndarray, List[float]]] = None   ,   __UpperCAmelCase				:			str = "epsilon"   ,   __UpperCAmelCase				:			str = "linspace"   ,   __UpperCAmelCase				:			int = 0   ,   ):





         '''simple docstring'''





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

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

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

         # setable values
         _A      =			None
         _A      =			np.linspace(0   ,   num_train_timesteps - 1   ,   __UpperCAmelCase   ,   dtype=__UpperCAmelCase	)[::-1].copy()
         _A      =			torch.from_numpy(__UpperCAmelCase	)
         _A      =			False

   @property
   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Union[float, torch.FloatTensor]	):





         '''simple docstring'''





         if isinstance(__UpperCAmelCase   ,   torch.Tensor	):
               _A      =			timestep.to(self.timesteps.device	)
         _A      =			(self.timesteps == timestep).nonzero().item()
         _A      =			self.sigmas[step_index]
         _A      =			sample / ((sigma**2 + 1) ** 0.5)
         _A      =			True
         return sample

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Union[str, torch.device] = None	):





         '''simple docstring'''





         _A      =			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":
               _A      =			np.linspace(0   ,   self.config.num_train_timesteps - 1   ,   __UpperCAmelCase   ,   dtype=__UpperCAmelCase	)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               _A      =			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
               _A      =			(np.arange(0   ,   __UpperCAmelCase	) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase	)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               _A      =			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
               _A      =			(np.arange(self.config.num_train_timesteps   ,   0   ,   -step_ratio	)).round().copy().astype(__UpperCAmelCase	)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

         _A      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         _A      =			np.interp(__UpperCAmelCase   ,   np.arange(0   ,   len(__UpperCAmelCase	)	)   ,   __UpperCAmelCase	)
         _A      =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
         _A      =			torch.from_numpy(__UpperCAmelCase	).to(device=__UpperCAmelCase	)
         if str(__UpperCAmelCase	).startswith("mps"	):
               # mps does not support float64
               _A      =			torch.from_numpy(__UpperCAmelCase	).to(__UpperCAmelCase   ,   dtype=torch.floataa	)
         else:
               _A      =			torch.from_numpy(__UpperCAmelCase	).to(device=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Union[float, torch.FloatTensor]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Optional[torch.Generator] = None   ,   __UpperCAmelCase				:			bool = True   ,   ):





         '''simple docstring'''





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

         _A      =			(self.timesteps == timestep).nonzero().item()
         _A      =			self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               _A      =			sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _A      =			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`'''	)

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

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

         _A      =			sigma_down - sigma

         _A      =			sample + derivative * dt

         _A      =			model_output.device
         _A      =			randn_tensor(model_output.shape   ,   dtype=model_output.dtype   ,   device=__UpperCAmelCase   ,   generator=__UpperCAmelCase	)

         _A      =			prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=__UpperCAmelCase   ,   pred_original_sample=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Tuple   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   ):





         '''simple docstring'''





         _A      =			self.sigmas.to(device=original_samples.device   ,   dtype=original_samples.dtype	)
         if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase	):
               # mps does not support float64
               _A      =			self.timesteps.to(original_samples.device   ,   dtype=torch.floataa	)
               _A      =			timesteps.to(original_samples.device   ,   dtype=torch.floataa	)
         else:
               _A      =			self.timesteps.to(original_samples.device	)
               _A      =			timesteps.to(original_samples.device	)

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

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

         _A      =			original_samples + noise * sigma
         return noisy_samples

   def __len__(	self				:			int	):





         '''simple docstring'''





         return self.config.num_train_timesteps














'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			data
         _A      =			None

   def __str__(	self				:			str	):





         '''simple docstring'''





         return f'''{self.data}'''





class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			None
         _A      =			None

   def __iter__(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.front
         while node:
               yield node.data
               _A      =			node.next

   def __len__(	self				:			Union[str, Any]	):





         '''simple docstring'''





         return len(tuple(iter(self	)	)	)

   def __str__(	self				:			int	):





         '''simple docstring'''





         return " <- ".join(str(__UpperCAmelCase	) for item in self	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return len(self	) == 0

   def 		lowerCAmelCase				(	self				:			int   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			Node(__UpperCAmelCase	)
         if self.is_empty():
               _A      =			_A      =			node
         else:
               assert isinstance(self.rear   ,   __UpperCAmelCase	)
               _A      =			node
               _A      =			node

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         if self.is_empty():
               raise IndexError("dequeue from empty queue"	)
         assert isinstance(self.front   ,   __UpperCAmelCase	)
         _A      =			self.front
         _A      =			self.front.next
         if self.front is None:
               _A      =			None
         return node.data

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			_A      =			None


if __name__ == "__main__":
    from doctest import testmod

    testmod()





