"""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


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






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



   '''simple docstring'''



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



def 			A__						(  lowercase_    :  Any			,   lowercase_    :  List[Any]=0.9_9_9			,   lowercase_    :  int="cosine"			,   )     ->       List[Any]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowercase_    :  int	):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(lowercase_    :  Union[str, Any]	):
         return math.exp(t * -1_2.0	)

   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     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase						,							UpperCAmelCase							):



   '''simple docstring'''



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







   @register_to_config
   def __init__(  self   ,							_SCREAMING_SNAKE_CASE = 1000   ,							_SCREAMING_SNAKE_CASE = 0.0001   ,							_SCREAMING_SNAKE_CASE = 0.02   ,							_SCREAMING_SNAKE_CASE = "linear"   ,							_SCREAMING_SNAKE_CASE = None   ,							_SCREAMING_SNAKE_CASE = "epsilon"   ,							_SCREAMING_SNAKE_CASE = "linspace"   ,							_SCREAMING_SNAKE_CASE = 0   ,							):


      '''simple docstring'''

      if trained_betas is not None:
         __lowerCamelCase								=				torch.tensor(_SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa  )
      elif beta_schedule == "linear":
         __lowerCamelCase								=				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.
         __lowerCamelCase								=				(
             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
         __lowerCamelCase								=				betas_for_alpha_bar(_SCREAMING_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(_SCREAMING_SNAKE_CASE  )

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







   @property
   def       _A							(  self  ):


      '''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   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if isinstance(_SCREAMING_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   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = 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   ,							_SCREAMING_SNAKE_CASE   ,							dtype=_SCREAMING_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   ,							_SCREAMING_SNAKE_CASE  ) * step_ratio).round()[::-1].copy().astype(_SCREAMING_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(_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\'.'''  )

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None   ,							_SCREAMING_SNAKE_CASE = True   ,							):


      '''simple docstring'''

      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  ):
         __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":
         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`'''  )

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

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

      __lowerCamelCase								=				sigma_down - sigma

      __lowerCamelCase								=				sample + derivative * dt

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

      __lowerCamelCase								=				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       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							):


      '''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(_SCREAMING_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  ):


      '''simple docstring'''

      return self.config.num_train_timesteps







"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :		List[Any]        =      {
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Optional[Any]        =      ['''DeformableDetrFeatureExtractor''']
     _SCREAMING_SNAKE_CASE  :		List[str]        =      ['''DeformableDetrImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      [
         '''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

     _SCREAMING_SNAKE_CASE  :		Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






