

"""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


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




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







 """simple docstring"""







 __UpperCAmelCase      :   torch.FloatTensor
 __UpperCAmelCase      :   Optional[torch.FloatTensor]  						=      None







def     _A    (    UpperCamelCase_	:			Any,     UpperCamelCase_	:			Dict=0.999,     UpperCamelCase_	:			Any="cosine",     )      ->		Union[str, Any]:
   '''simple docstring'''


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCamelCase_	:			Optional[int]):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

   elif alpha_transform_type == "exp":

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

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

   __lowercase								=		[]
   for i in range(_A):
      __lowercase								=		i / num_diffusion_timesteps
      __lowercase								=		(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    _lowerCAmelCase    (      lowerCAmelCase__	,lowerCAmelCase__  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      [e.name for e in KarrasDiffusionSchedulers]
 __UpperCAmelCase      :   Tuple  						=      1


 @register_to_config
 def __init__(     self				:     List[str],   UpperCAmelCase__				:     Dict = 1_0_0_0,   UpperCAmelCase__				:     Optional[int] = 0.0_001,   UpperCAmelCase__				:     Optional[int] = 0.02,   UpperCAmelCase__				:     int = "linear",   UpperCAmelCase__				:     Any = None,   UpperCAmelCase__				:     str = "epsilon",   UpperCAmelCase__				:     Any = "linspace",   UpperCAmelCase__				:     str = 0,   ):
    if trained_betas is not None:
       __lowercase								=		torch.tensor(_SCREAMING_SNAKE_CASE,   dtype=torch.floataa      )
    elif beta_schedule == "linear":
       __lowercase								=		torch.linspace(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   dtype=torch.floataa      )
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowercase								=		(
           torch.linspace(beta_start**0.5,   beta_end**0.5,   _SCREAMING_SNAKE_CASE,   dtype=torch.floataa      ) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowercase								=		betas_for_alpha_bar(_SCREAMING_SNAKE_CASE      )
    else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

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

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

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


 @property
 def    _lowercase					(     self				:     Union[str, Any]      ):
    # 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    _lowercase					(     self				:     Dict,   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Union[str, Any]      ):
    if isinstance(_SCREAMING_SNAKE_CASE,   torch.Tensor      ):
       __lowercase								=		timestep.to(self.timesteps.device      )
    __lowercase								=		(self.timesteps == timestep).nonzero().item()
    __lowercase								=		self.sigmas[step_index]
    __lowercase								=		sample / ((sigma**2 + 1) ** 0.5)
    __lowercase								=		True
    return sample


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     str = None      ):
    __lowercase								=		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":
       __lowercase								=		np.linspace(0,   self.config.num_train_timesteps - 1,   _SCREAMING_SNAKE_CASE,   dtype=_SCREAMING_SNAKE_CASE      )[
           ::-1
       ].copy()
    elif self.config.timestep_spacing == "leading":
       __lowercase								=		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
       __lowercase								=		(np.arange(0,   _SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE      )
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       __lowercase								=		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
       __lowercase								=		(np.arange(self.config.num_train_timesteps,   0,   -step_ratio      )).round().copy().astype(_SCREAMING_SNAKE_CASE      )
       timesteps -= 1
    else:
       raise ValueError(
           F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""      )

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


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Any = None,   UpperCAmelCase__				:     str = True,   ):

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

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

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

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

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

    __lowercase								=		sigma_down - sigma

    __lowercase								=		sample + derivative * dt

    __lowercase								=		model_output.device
    __lowercase								=		randn_tensor(model_output.shape,   dtype=model_output.dtype,   device=_SCREAMING_SNAKE_CASE,   generator=_SCREAMING_SNAKE_CASE      )

    __lowercase								=		prev_sample + noise * sigma_up

    if not return_dict:
       return (prev_sample,)

    return EulerAncestralDiscreteSchedulerOutput(
        prev_sample=_SCREAMING_SNAKE_CASE,   pred_original_sample=_SCREAMING_SNAKE_CASE      )


 def    _lowercase					(     self				:     Tuple,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Optional[Any],   ):
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowercase								=		self.sigmas.to(device=original_samples.device,   dtype=original_samples.dtype      )
    if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE      ):
       # mps does not support float64
       __lowercase								=		self.timesteps.to(original_samples.device,   dtype=torch.floataa      )
       __lowercase								=		timesteps.to(original_samples.device,   dtype=torch.floataa      )
    else:
       __lowercase								=		self.timesteps.to(original_samples.device      )
       __lowercase								=		timesteps.to(original_samples.device      )

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

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

    __lowercase								=		original_samples + noise * sigma
    return noisy_samples



 def __len__(     self				:     Dict      ):
    return self.config.num_train_timesteps







from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
