






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


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  __UpperCAmelCase						(SCREAMING_SNAKE_CASE__       ):
			__snake_case							: Dict							=				42
			__snake_case							: Tuple							=				None

def 			__lowerCamelCase     (						snake_case__			,snake_case__=0.999			,snake_case__="cosine"			,)				->    Optional[int]:






						"""simple docstring"""





						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						_SCREAMING_SNAKE_CASE       =    []
						for i in range(__lowerCAmelCase						):
												_SCREAMING_SNAKE_CASE       =    i / num_diffusion_timesteps
												_SCREAMING_SNAKE_CASE       =    (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  __UpperCAmelCase						(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__       ):
			__snake_case							: Tuple							=				[e.name for e in KarrasDiffusionSchedulers]
			__snake_case							: Any							=				1







			@register_to_config
			def __init__(      self:    List[Any] ,      UpperCAmelCase_:    str = 1_000 ,      UpperCAmelCase_:    Dict = 0.00_01 ,      UpperCAmelCase_:    Union[str, Any] = 0.02 ,      UpperCAmelCase_:    Optional[int] = "linear" ,      UpperCAmelCase_:    Optional[int] = None ,      UpperCAmelCase_:    List[str] = "epsilon" ,      UpperCAmelCase_:    Union[str, Any] = "linear" ,      UpperCAmelCase_:    Union[str, Any] = False ,      UpperCAmelCase_:    List[str] = "linspace" ,      UpperCAmelCase_:    Any = 0 ,      ):







									'''simple docstring'''




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

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

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

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







			@property
			def 				UpperCamelCase (      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 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    Optional[Any]	):







									'''simple docstring'''




									if isinstance(lowerCAmelCase__ ,      torch.Tensor	):
															_SCREAMING_SNAKE_CASE       =    timestep.to(self.timesteps.device	)
									_SCREAMING_SNAKE_CASE       =    (self.timesteps == timestep).nonzero().item()
									_SCREAMING_SNAKE_CASE       =    self.sigmas[step_index]

									_SCREAMING_SNAKE_CASE       =    sample / ((sigma**2 + 1) ** 0.5)

									_SCREAMING_SNAKE_CASE       =    True
									return sample







			def 				UpperCamelCase (      self:    List[Any] ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    int = None	):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									_SCREAMING_SNAKE_CASE       =    np.log(lowerCAmelCase__	)

									if self.config.interpolation_type == "linear":
															_SCREAMING_SNAKE_CASE       =    np.interp(lowerCAmelCase__ ,      np.arange(0 ,      len(lowerCAmelCase__	)	) ,      lowerCAmelCase__	)
									elif self.config.interpolation_type == "log_linear":
															_SCREAMING_SNAKE_CASE       =    torch.linspace(np.log(sigmas[-1]	) ,      np.log(sigmas[0]	) ,      num_inference_steps + 1	).exp()
									else:
															raise ValueError(
															    F'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
															    """ 'linear' or 'log_linear'"""	)

									if self.use_karras_sigmas:
															_SCREAMING_SNAKE_CASE       =    self._convert_to_karras(in_sigmas=lowerCAmelCase__ ,      num_inference_steps=self.num_inference_steps	)
															_SCREAMING_SNAKE_CASE       =    np.array([self._sigma_to_t(lowerCAmelCase__ ,      lowerCAmelCase__	) for sigma in sigmas]	)

									_SCREAMING_SNAKE_CASE       =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
									_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)
									if str(lowerCAmelCase__	).startswith("""mps"""	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__ ,      dtype=torch.floataa	)
									else:
															_SCREAMING_SNAKE_CASE       =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    np.log(lowerCAmelCase__	)

									# get distribution
									_SCREAMING_SNAKE_CASE       =    log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									_SCREAMING_SNAKE_CASE       =    np.cumsum((dists >= 0) ,      axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
									_SCREAMING_SNAKE_CASE       =    low_idx + 1

									_SCREAMING_SNAKE_CASE       =    log_sigmas[low_idx]
									_SCREAMING_SNAKE_CASE       =    log_sigmas[high_idx]

									# interpolate sigmas
									_SCREAMING_SNAKE_CASE       =    (low - log_sigma) / (low - high)
									_SCREAMING_SNAKE_CASE       =    np.clip(lowerCAmelCase__ ,      0 ,      1	)

									# transform interpolation to time range
									_SCREAMING_SNAKE_CASE       =    (1 - w) * low_idx + w * high_idx
									_SCREAMING_SNAKE_CASE       =    t.reshape(sigma.shape	)
									return t







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    List[str] ,      UpperCAmelCase_:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    in_sigmas[-1].item()
									_SCREAMING_SNAKE_CASE       =    in_sigmas[0].item()

									_SCREAMING_SNAKE_CASE       =    7.0  # 7.0 is the value used in the paper
									_SCREAMING_SNAKE_CASE       =    np.linspace(0 ,      1 ,      lowerCAmelCase__	)
									_SCREAMING_SNAKE_CASE       =    sigma_min ** (1 / rho)
									_SCREAMING_SNAKE_CASE       =    sigma_max ** (1 / rho)
									_SCREAMING_SNAKE_CASE       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Dict ,      UpperCAmelCase_:    List[str] ,      UpperCAmelCase_:    Union[str, Any] = 0.0 ,      UpperCAmelCase_:    int = 0.0 ,      UpperCAmelCase_:    Optional[Any] = float("""inf"""	) ,      UpperCAmelCase_:    str = 1.0 ,      UpperCAmelCase_:    Dict = None ,      UpperCAmelCase_:    Tuple = 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	):
															_SCREAMING_SNAKE_CASE       =    timestep.to(self.timesteps.device	)

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

									_SCREAMING_SNAKE_CASE       =    min(s_churn / (len(self.sigmas	) - 1) ,      2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

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

									_SCREAMING_SNAKE_CASE       =    noise * s_noise
									_SCREAMING_SNAKE_CASE       =    sigma * (gamma + 1)

									if gamma > 0:
															_SCREAMING_SNAKE_CASE       =    sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
															_SCREAMING_SNAKE_CASE       =    model_output
									elif self.config.prediction_type == "epsilon":
															_SCREAMING_SNAKE_CASE       =    sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															_SCREAMING_SNAKE_CASE       =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
															raise ValueError(
															    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'	)

									# 2. Convert to an ODE derivative
									_SCREAMING_SNAKE_CASE       =    (sample - pred_original_sample) / sigma_hat

									_SCREAMING_SNAKE_CASE       =    self.sigmas[step_index + 1] - sigma_hat

									_SCREAMING_SNAKE_CASE       =    sample + derivative * dt

									if not return_dict:
															return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__ ,      pred_original_sample=lowerCAmelCase__	)







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    List[str] ,      UpperCAmelCase_:    str ,      ):







									'''simple docstring'''




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

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

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

									_SCREAMING_SNAKE_CASE       =    original_samples + noise * sigma
									return noisy_samples







			def __len__(      self:    int	):







									'''simple docstring'''




									return self.config.num_train_timesteps







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_SCREAMING_SNAKE_CASE						 =     logging.get_logger(__name__)







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  warnings.warn(
      "The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
      " Please use LayoutLMv3ImageProcessor instead."					,     lowerCAmelCase__					,     )
  super().__init__(*lowerCAmelCase__					,     **lowerCAmelCase__   )



