





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase													=			{
    """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase													=			["""DeformableDetrFeatureExtractor"""]
      __lowercase													=			["""DeformableDetrImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase													=			[
          """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DeformableDetrForObjectDetection""",
          """DeformableDetrModel""",
          """DeformableDetrPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
            from .image_processing_deformable_detr import DeformableDetrImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deformable_detr import (
                DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeformableDetrForObjectDetection,
                DeformableDetrModel,
                DeformableDetrPreTrainedModel,
            )

else:
      import sys

      __lowercase													=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	42
    _lowerCAmelCase     =	None


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]     ,							__lowercase:		Optional[Any]=0.999     ,							__lowercase:		List[str]="cosine"     ,							):



  '''simple docstring'''






  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  __lowerCAmelCase			=				[]
  for i in range(__lowercase):
    __lowerCAmelCase			=				i / num_diffusion_timesteps
    __lowerCAmelCase			=				(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   __snake_case				(			_a       ,				_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	[e.name for e in KarrasDiffusionSchedulers]
    _lowerCAmelCase     =	1





    @register_to_config
    def __init__(   self						:		Optional[int]			,						snake_case						:		int = 1_000			,						snake_case						:		float = 0.0001			,						snake_case						:		float = 0.02			,						snake_case						:		str = "linear"			,						snake_case						:		Optional[Union[np.ndarray, List[float]]] = None			,						snake_case						:		Optional[bool] = False			,						snake_case						:		str = "epsilon"			,						snake_case						:		str = "linspace"			,						snake_case						:		int = 0			,						):

      """simple docstring"""





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

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

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

      # setable values
      __lowerCAmelCase			=				None
      __lowerCAmelCase			=				use_karras_sigmas
      self.set_timesteps(snake_case			,						snake_case							)
      __lowerCAmelCase			=				[]
      __lowerCAmelCase			=				False





    @property
    def 	A		(   self						:		Any							):

      """simple docstring"""





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

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





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		Union[float, torch.FloatTensor]							):

      """simple docstring"""





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





    def 	A		(   self						:		Optional[int]			,						snake_case						:		List[str]			,						snake_case						:		int			,						snake_case						:		Any							):

      """simple docstring"""





      def lms_derivative(snake_case						:		Union[str, Any]							):
        __lowerCAmelCase			=				1.0
        for k in range(snake_case							):
          if current_order == k:
            continue
          prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

      __lowerCAmelCase			=				integrate.quad(snake_case			,						self.sigmas[t]			,						self.sigmas[t + 1]			,						epsrel=1e-4							)[0]

      return integrated_coeff





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

      """simple docstring"""





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

      __lowerCAmelCase			=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
      __lowerCAmelCase			=				np.log(snake_case							)
      __lowerCAmelCase			=				np.interp(snake_case			,						np.arange(0			,						len(snake_case							)							)			,						snake_case							)

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

      __lowerCAmelCase			=				np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)

      __lowerCAmelCase			=				torch.from_numpy(snake_case							).to(device=snake_case							)
      if str(snake_case							).startswith('''mps'''							):
        # mps does not support float64
        __lowerCAmelCase			=				torch.from_numpy(snake_case							).to(snake_case			,						dtype=torch.floataa							)
      else:
        __lowerCAmelCase			=				torch.from_numpy(snake_case							).to(device=snake_case							)

      __lowerCAmelCase			=				[]





    def 	A		(   self						:		List[Any]			,						snake_case						:		List[Any]			,						snake_case						:		Dict							):

      """simple docstring"""





      __lowerCAmelCase			=				np.log(snake_case							)

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

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

      __lowerCAmelCase			=				log_sigmas[low_idx]
      __lowerCAmelCase			=				log_sigmas[high_idx]

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

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





    def 	A		(   self						:		List[Any]			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





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

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





    def 	A		(   self						:		List[str]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		Union[float, torch.FloatTensor]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		int = 4			,						snake_case						:		bool = True			,						):

      """simple docstring"""





      if not self.is_scale_input_called:
        warnings.warn(
            '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
            '''See `StableDiffusionPipeline` for a usage example.'''							)

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

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        __lowerCAmelCase			=				sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        __lowerCAmelCase			=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
        __lowerCAmelCase			=				model_output
      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
      __lowerCAmelCase			=				(sample - pred_original_sample) / sigma
      self.derivatives.append(snake_case							)
      if len(self.derivatives							) > order:
        self.derivatives.pop(0							)

      # 3. Compute linear multistep coefficients
      __lowerCAmelCase			=				min(step_index + 1			,						snake_case							)
      __lowerCAmelCase			=				[self.get_lms_coefficient(snake_case			,						snake_case			,						snake_case							) for curr_order in range(snake_case							)]

      # 4. Compute previous sample based on the derivatives path
      __lowerCAmelCase			=				sample + sum(
          coeff * derivative for coeff, derivative in zip(snake_case			,						reversed(self.derivatives							)							)							)

      if not return_dict:
        return (prev_sample,)

      return LMSDiscreteSchedulerOutput(prev_sample=snake_case			,						pred_original_sample=snake_case							)





    def 	A		(   self						:		Optional[int]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor			,						):

      """simple docstring"""





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

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

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

      __lowerCAmelCase			=				original_samples + noise * sigma
      return noisy_samples





    def __len__(   self						:		Dict							):

      """simple docstring"""





      return self.config.num_train_timesteps
