






"""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


_UpperCamelCase   :						Optional[Any]     						= logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class __a   (   snake_case_				):
  __lowercase    :    torch.FloatTensor
  __lowercase    :    Optional[torch.FloatTensor]						=					None






def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				Any=0.999     ,	UpperCamelCase_							:				Dict="cosine"     ,	)  ->		Optional[Any]:
 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

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






class __a   (   snake_case_ ,snake_case_				):
  __lowercase    :    str						=					[e.name for e in KarrasDiffusionSchedulers]
  __lowercase    :    str						=					1



  @register_to_config
  def __init__(self  :		Tuple				,			lowercase_  :		int = 1_000				,			lowercase_  :		float = 0.0001				,			lowercase_  :		float = 0.02				,			lowercase_  :		str = "linear"				,			lowercase_  :		Optional[Union[np.ndarray, List[float]]] = None				,			lowercase_  :		str = "epsilon"				,			lowercase_  :		str = "linear"				,			lowercase_  :		Optional[bool] = False				,			lowercase_  :		str = "linspace"				,			lowercase_  :		int = 0				,			)		->							Optional[Any]:
   if trained_betas is not None:
    __A	:       str       =	torch.tensor(lowercase_				,			dtype=torch.floataa   )
   elif beta_schedule == "linear":
    __A	:       Any       =	torch.linspace(lowercase_				,			lowercase_				,			lowercase_				,			dtype=torch.floataa   )
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __A	:       Tuple       =	(
        torch.linspace(beta_start**0.5				,			beta_end**0.5				,			lowercase_				,			dtype=torch.floataa   ) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __A	:       List[Any]       =	betas_for_alpha_bar(lowercase_   )
   else:
    raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

   __A	:       Optional[Any]       =	1.0 - self.betas
   __A	:       Optional[Any]       =	torch.cumprod(self.alphas				,			dim=0   )

   __A	:       List[Any]       =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
   __A	:       List[str]       =	np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
   __A	:       Dict       =	torch.from_numpy(lowercase_   )

   # setable values
   __A	:       Optional[Any]       =	None
   __A	:       str       =	np.linspace(0				,			num_train_timesteps - 1				,			lowercase_				,			dtype=lowercase_   )[::-1].copy()
   __A	:       Any       =	torch.from_numpy(lowercase_   )
   __A	:       Any       =	False
   __A	:       Optional[Any]       =	use_karras_sigmas



  @property
  def 						A_				(self  :		str   )		->							List[str]:
   # 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  :		Optional[Any]				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		Union[float, torch.FloatTensor]   )		->							torch.FloatTensor:
   if isinstance(lowercase_				,			torch.Tensor   ):
    __A	:       str       =	timestep.to(self.timesteps.device   )
   __A	:       str       =	(self.timesteps == timestep).nonzero().item()
   __A	:       Tuple       =	self.sigmas[step_index]

   __A	:       Any       =	sample / ((sigma**2 + 1) ** 0.5)

   __A	:       Union[str, Any]       =	True
   return sample



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		int				,			lowercase_  :		Union[str, torch.device] = None   )		->							List[Any]:
   __A	:       Tuple       =	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	:       Optional[int]       =	np.linspace(0				,			self.config.num_train_timesteps - 1				,			lowercase_				,			dtype=lowercase_   )[
        ::-1
    ].copy()
   elif self.config.timestep_spacing == "leading":
    __A	:       Tuple       =	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	:       int       =	(np.arange(0				,			lowercase_   ) * step_ratio).round()[::-1].copy().astype(lowercase_   )
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    __A	:       Any       =	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	:       int       =	(np.arange(self.config.num_train_timesteps				,			0				,			-step_ratio   )).round().copy().astype(lowercase_   )
    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	:       Optional[int]       =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
   __A	:       List[str]       =	np.log(lowercase_   )

   if self.config.interpolation_type == "linear":
    __A	:       Any       =	np.interp(lowercase_				,			np.arange(0				,			len(lowercase_   )   )				,			lowercase_   )
   elif self.config.interpolation_type == "log_linear":
    __A	:       int       =	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:
    __A	:       int       =	self._convert_to_karras(in_sigmas=lowercase_				,			num_inference_steps=self.num_inference_steps   )
    __A	:       List[str]       =	np.array([self._sigma_to_t(lowercase_				,			lowercase_   ) for sigma in sigmas]   )

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



  def 						A_				(self  :		Optional[int]				,			lowercase_  :		Dict				,			lowercase_  :		Optional[int]   )		->							Optional[Any]:
   # get log sigma
   __A	:       str       =	np.log(lowercase_   )

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

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

   __A	:       List[str]       =	log_sigmas[low_idx]
   __A	:       Optional[int]       =	log_sigmas[high_idx]

   # interpolate sigmas
   __A	:       Optional[int]       =	(low - log_sigma) / (low - high)
   __A	:       Tuple       =	np.clip(lowercase_				,			0				,			1   )

   # transform interpolation to time range
   __A	:       Optional[Any]       =	(1 - w) * low_idx + w * high_idx
   __A	:       Any       =	t.reshape(sigma.shape   )
   return t



  def 						A_				(self  :		List[str]				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		List[str]   )		->							torch.FloatTensor:
   __A	:       float       =	in_sigmas[-1].item()
   __A	:       float       =	in_sigmas[0].item()

   __A	:       Optional[Any]       =	7.0  # 7.0 is the value used in the paper
   __A	:       int       =	np.linspace(0				,			1				,			lowercase_   )
   __A	:       Any       =	sigma_min ** (1 / rho)
   __A	:       Optional[int]       =	sigma_max ** (1 / rho)
   __A	:       int       =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas



  def 						A_				(self  :		Any				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		Union[float, torch.FloatTensor]				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		float = 0.0				,			lowercase_  :		float = 0.0				,			lowercase_  :		float = float('inf'   )				,			lowercase_  :		float = 1.0				,			lowercase_  :		Optional[torch.Generator] = None				,			lowercase_  :		bool = True				,			)		->							Union[EulerDiscreteSchedulerOutput, Tuple]:

   if (
       isinstance(lowercase_				,			lowercase_   )
       or isinstance(lowercase_				,			torch.IntTensor   )
       or isinstance(lowercase_				,			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(lowercase_				,			torch.Tensor   ):
    __A	:       List[str]       =	timestep.to(self.timesteps.device   )

   __A	:       Tuple       =	(self.timesteps == timestep).nonzero().item()
   __A	:       Optional[int]       =	self.sigmas[step_index]

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

   __A	:       int       =	randn_tensor(
       model_output.shape				,			dtype=model_output.dtype				,			device=model_output.device				,			generator=lowercase_   )

   __A	:       Tuple       =	noise * s_noise
   __A	:       Optional[int]       =	sigma * (gamma + 1)

   if gamma > 0:
    __A	:       str       =	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":
    __A	:       Dict       =	model_output
   elif self.config.prediction_type == "epsilon":
    __A	:       List[Any]       =	sample - sigma_hat * model_output
   elif self.config.prediction_type == "v_prediction":
    # * c_out + input * c_skip
    __A	:       str       =	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
   __A	:       List[Any]       =	(sample - pred_original_sample) / sigma_hat

   __A	:       Any       =	self.sigmas[step_index + 1] - sigma_hat

   __A	:       int       =	sample + derivative * dt

   if not return_dict:
    return (prev_sample,)

   return EulerDiscreteSchedulerOutput(prev_sample=lowercase_				,			pred_original_sample=lowercase_   )



  def 						A_				(self  :		int				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		torch.FloatTensor				,			)		->							torch.FloatTensor:
   # Make sure sigmas and timesteps have the same device and dtype as original_samples
   __A	:       str       =	self.sigmas.to(device=original_samples.device				,			dtype=original_samples.dtype   )
   if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_   ):
    # mps does not support float64
    __A	:       Optional[int]       =	self.timesteps.to(original_samples.device				,			dtype=torch.floataa   )
    __A	:       Union[str, Any]       =	timesteps.to(original_samples.device				,			dtype=torch.floataa   )
   else:
    __A	:       Any       =	self.timesteps.to(original_samples.device   )
    __A	:       Union[str, Any]       =	timesteps.to(original_samples.device   )

   __A	:       Any       =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

   __A	:       int       =	sigmas[step_indices].flatten()
   while len(sigma.shape   ) < len(original_samples.shape   ):
    __A	:       Optional[Any]       =	sigma.unsqueeze(-1   )

   __A	:       int       =	original_samples + noise * sigma
   return noisy_samples



  def __len__(self  :		int   )		->							int:
   return self.config.num_train_timesteps













"""simple docstring"""


import warnings

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


_UpperCamelCase   :						str     						= logging.get_logger(__name__)






class __a   (   snake_case_				):



  def __init__(self  :		Union[str, Any]				,			*lowercase_  :		Any				,			**lowercase_  :		Optional[Any]   )		->							None:
   warnings.warn(
       'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
       ' Please use LayoutLMv3ImageProcessor instead.'				,			lowercase_				,			)
   super().__init__(*lowercase_				,			**lowercase_   )





