
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


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

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




def 					lowerCamelCase__					(    a__	:	int						,   a__	:	Dict=0.999						,   a__	:	int="cosine"						,   )		->						Optional[int]:







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(a__	:	List[str]  ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(a__	:	List[str]  ):
                     return math.exp(t * -12.0  )

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

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







class 		lowercase_							(							__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE					):
 A__    :					Union[str, Any]    =     [e.name for e in KarrasDiffusionSchedulers]
 A__    :					int    =     1

 @register_to_config
 def __init__(   self  ,						__UpperCamelCase = 1_0_0_0  ,						__UpperCamelCase = 0.0_001  ,						__UpperCamelCase = 0.02  ,						__UpperCamelCase = "linear"  ,						__UpperCamelCase = None  ,						__UpperCamelCase = "epsilon"  ,						__UpperCamelCase = "linspace"  ,						__UpperCamelCase = 0  ,						):




        """simple docstring"""


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

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

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

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

 @property
 def lowerCamelCase_					(   self  ):




        """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  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ):




        """simple docstring"""


        UpperCamelCase_											=							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":
               UpperCamelCase_											=							np.linspace(0  ,						self.config.num_train_timesteps - 1  ,						__UpperCamelCase  ,						dtype=__UpperCamelCase  )[
                   ::-1
               ].copy()
        elif self.config.timestep_spacing == "leading":
               UpperCamelCase_											=							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
               UpperCamelCase_											=							(np.arange(0  ,						__UpperCamelCase  ) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase  )
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               UpperCamelCase_											=							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
               UpperCamelCase_											=							(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\'.'''  )

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = 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  ):
               UpperCamelCase_											=							timestep.to(self.timesteps.device  )

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

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

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

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

        UpperCamelCase_											=							sigma_down - sigma

        UpperCamelCase_											=							sample + derivative * dt

        UpperCamelCase_											=							model_output.device
        UpperCamelCase_											=							randn_tensor(model_output.shape  ,						dtype=model_output.dtype  ,						device=__UpperCamelCase  ,						generator=__UpperCamelCase  )

        UpperCamelCase_											=							prev_sample + noise * sigma_up

        if not return_dict:
               return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=__UpperCamelCase  ,						pred_original_sample=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							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
               UpperCamelCase_											=							self.timesteps.to(original_samples.device  ,						dtype=torch.floataa  )
               UpperCamelCase_											=							timesteps.to(original_samples.device  ,						dtype=torch.floataa  )
        else:
               UpperCamelCase_											=							self.timesteps.to(original_samples.device  )
               UpperCamelCase_											=							timesteps.to(original_samples.device  )

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

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

        UpperCamelCase_											=							original_samples + noise * sigma
        return noisy_samples

 def __len__(   self  ):




        """simple docstring"""


        return self.config.num_train_timesteps






from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class 					__UpperCamelCase					:







	def __init__( self:			Optional[Any]	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     data
						__magic_name__				=     None







	def __str__( self:			Optional[int]   ):

						'''simple docstring'''





						return F'{self.data}'




class 					__UpperCamelCase					:







	def __init__( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     None
						__magic_name__				=     None







	def __iter__( self:			str   ):

						'''simple docstring'''





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







	def __len__( self:			Any   ):

						'''simple docstring'''





						return len(tuple(iter(self   )   )   )







	def __str__( self:			List[str]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						return len(self   ) == 0







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     Node(__UpperCamelCase   )
						if self.is_empty():
											__magic_name__				=     __magic_name__				=     node
						else:
											assert isinstance(self.rear	,			__UpperCamelCase   )
											__magic_name__				=     node
											__magic_name__				=     node







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     __magic_name__				=     None


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

						testmod()




