
'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def snake_case_    (				lowercase__      ,					lowercase__=0.999      ,					lowercase__="cosine"      ,					):
  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  UpperCAmelCase__			:				List[Any]												=       []
  for i in range(lowercase__		):
    UpperCAmelCase__			:				Dict												=       i / num_diffusion_timesteps
    UpperCAmelCase__			:				str												=       (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        UpperCAmelCase_       (						A				,							A							):

 '''simple docstring'''
 lowercase_				:						List[Any]				=       [e.name for e in KarrasDiffusionSchedulers]
 lowercase_				:						Union[str, Any]				=       2







 @register_to_config
 def __init__(				self		:     List[Any] ,							snake_case__		:     int = 10_00 ,							snake_case__		:     float = 0.00085 ,							snake_case__		:     float = 0.012 ,							snake_case__		:     str = "linear" ,							snake_case__		:     Optional[Union[np.ndarray, List[float]]] = None ,							snake_case__		:     str = "epsilon" ,							snake_case__		:     str = "linspace" ,							snake_case__		:     int = 0 ,							):



   '''simple docstring'''




   if trained_betas is not None:
     UpperCAmelCase__			:				str												=       torch.tensor(snake_case__ ,							dtype=torch.floataa   )
   elif beta_schedule == "linear":
     UpperCAmelCase__			:				Tuple												=       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.
     UpperCAmelCase__			:				List[Any]												=       (
         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
     UpperCAmelCase__			:				Dict												=       betas_for_alpha_bar(snake_case__   )
   else:
     raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""   )

   UpperCAmelCase__			:				Dict												=       1.0 - self.betas
   UpperCAmelCase__			:				Tuple												=       torch.cumprod(self.alphas ,							dim=0   )

   #  set all values
   self.set_timesteps(snake_case__ ,							snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[Any] ,							snake_case__		:     Optional[int] ,							snake_case__		:     Dict=None   ):



   '''simple docstring'''




   if schedule_timesteps is None:
     UpperCAmelCase__			:				List[str]												=       self.timesteps

   UpperCAmelCase__			:				int												=       (schedule_timesteps == timestep).nonzero()

   # The sigma index that is taken for the **very** first `step`
   # is always the second index (or the last index if there is only 1)
   # This way we can ensure we don't accidentally skip a sigma in
   # case we start in the middle of the denoising schedule (e.g. for image-to-image)
   if len(self._index_counter   ) == 0:
     UpperCAmelCase__			:				Tuple												=       1 if len(snake_case__   ) > 1 else 0
   else:
     UpperCAmelCase__			:				Optional[int]												=       timestep.cpu().item() if torch.is_tensor(snake_case__   ) else timestep
     UpperCAmelCase__			:				Optional[Any]												=       self._index_counter[timestep_int]

   return indices[pos].item()







 @property
 def        UpperCamelCase     (				self		:     str   ):



   '''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		:     int ,							snake_case__		:     torch.FloatTensor ,							snake_case__		:     Union[float, torch.FloatTensor] ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       self.index_for_timestep(snake_case__   )

   if self.state_in_first_order:
     UpperCAmelCase__			:				Any												=       self.sigmas[step_index]
   else:
     UpperCAmelCase__			:				Optional[Any]												=       self.sigmas_interpol[step_index - 1]

   UpperCAmelCase__			:				List[str]												=       sample / ((sigma**2 + 1) ** 0.5)
   return sample







 def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     int ,							snake_case__		:     Union[str, torch.device] = None ,							snake_case__		:     Optional[int] = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       num_inference_steps

   UpperCAmelCase__			:				Optional[int]												=       num_train_timesteps or self.config.num_train_timesteps

   # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
   if self.config.timestep_spacing == "linspace":
     UpperCAmelCase__			:				Union[str, Any]												=       np.linspace(0 ,							num_train_timesteps - 1 ,							snake_case__ ,							dtype=snake_case__   )[::-1].copy()
   elif self.config.timestep_spacing == "leading":
     UpperCAmelCase__			:				int												=       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
     UpperCAmelCase__			:				int												=       (np.arange(0 ,							snake_case__   ) * step_ratio).round()[::-1].copy().astype(snake_case__   )
     timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
     UpperCAmelCase__			:				Dict												=       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
     UpperCAmelCase__			:				Optional[Any]												=       (np.arange(snake_case__ ,							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'."""   )

   UpperCAmelCase__			:				Tuple												=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
   UpperCAmelCase__			:				List[str]												=       torch.from_numpy(np.log(snake_case__   )   ).to(snake_case__   )

   UpperCAmelCase__			:				Union[str, Any]												=       np.interp(snake_case__ ,							np.arange(0 ,							len(snake_case__   )   ) ,							snake_case__   )
   UpperCAmelCase__			:				Any												=       np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
   UpperCAmelCase__			:				Any												=       torch.from_numpy(snake_case__   ).to(device=snake_case__   )

   # compute up and down sigmas
   UpperCAmelCase__			:				Any												=       sigmas.roll(-1   )
   UpperCAmelCase__			:				Tuple												=       0.0
   UpperCAmelCase__			:				Union[str, Any]												=       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
   UpperCAmelCase__			:				Optional[int]												=       (sigmas_next**2 - sigmas_up**2) ** 0.5
   UpperCAmelCase__			:				Optional[int]												=       0.0

   # compute interpolated sigmas
   UpperCAmelCase__			:				Tuple												=       sigmas.log().lerp(sigmas_down.log() ,							0.5   ).exp()
   UpperCAmelCase__			:				str												=       0.0

   # set sigmas
   UpperCAmelCase__			:				Union[str, Any]												=       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
   UpperCAmelCase__			:				Optional[Any]												=       torch.cat(
       [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
   UpperCAmelCase__			:				Optional[Any]												=       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
   UpperCAmelCase__			:				str												=       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

   if str(snake_case__   ).startswith("mps"   ):
     # mps does not support float64
     UpperCAmelCase__			:				List[str]												=       torch.from_numpy(snake_case__   ).to(snake_case__ ,							dtype=torch.floataa   )
   else:
     UpperCAmelCase__			:				List[str]												=       torch.from_numpy(snake_case__   ).to(snake_case__   )

   UpperCAmelCase__			:				Tuple												=       self.sigma_to_t(snake_case__   ).to(snake_case__ ,							dtype=timesteps.dtype   )
   UpperCAmelCase__			:				List[Any]												=       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,							dim=-1   ).flatten()

   UpperCAmelCase__			:				Union[str, Any]												=       torch.cat([timesteps[:1], interleaved_timesteps]   )

   UpperCAmelCase__			:				List[str]												=       None

   # for exp beta schedules, such as the one for `pipeline_shap_e.py`
   # we need an index counter
   UpperCAmelCase__			:				Union[str, Any]												=       defaultdict(snake_case__   )







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       sigma.log()

   # get distribution
   UpperCAmelCase__			:				Tuple												=       log_sigma - self.log_sigmas[:, None]

   # get sigmas range
   UpperCAmelCase__			:				int												=       dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
   UpperCAmelCase__			:				int												=       low_idx + 1

   UpperCAmelCase__			:				int												=       self.log_sigmas[low_idx]
   UpperCAmelCase__			:				List[str]												=       self.log_sigmas[high_idx]

   # interpolate sigmas
   UpperCAmelCase__			:				Optional[Any]												=       (low - log_sigma) / (low - high)
   UpperCAmelCase__			:				Optional[int]												=       w.clamp(0 ,							1   )

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







 @property
 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   return self.sample is None







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     Union[torch.FloatTensor, np.ndarray] ,							snake_case__		:     Union[float, torch.FloatTensor] ,							snake_case__		:     Union[torch.FloatTensor, np.ndarray] ,							snake_case__		:     Optional[torch.Generator] = None ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       self.index_for_timestep(snake_case__   )

   # advance index counter by 1
   UpperCAmelCase__			:				Dict												=       timestep.cpu().item() if torch.is_tensor(snake_case__   ) else timestep
   self._index_counter[timestep_int] += 1

   if self.state_in_first_order:
     UpperCAmelCase__			:				List[Any]												=       self.sigmas[step_index]
     UpperCAmelCase__			:				Any												=       self.sigmas_interpol[step_index]
     UpperCAmelCase__			:				Tuple												=       self.sigmas_up[step_index]
     UpperCAmelCase__			:				int												=       self.sigmas_down[step_index - 1]
   else:
     # 2nd order / KPDM2's method
     UpperCAmelCase__			:				Dict												=       self.sigmas[step_index - 1]
     UpperCAmelCase__			:				List[Any]												=       self.sigmas_interpol[step_index - 1]
     UpperCAmelCase__			:				str												=       self.sigmas_up[step_index - 1]
     UpperCAmelCase__			:				Union[str, Any]												=       self.sigmas_down[step_index - 1]

   # currently only gamma=0 is supported. This usually works best anyways.
   # We can support gamma in the future but then need to scale the timestep before
   # passing it to the model which requires a change in API
   UpperCAmelCase__			:				List[str]												=       0
   UpperCAmelCase__			:				int												=       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

   UpperCAmelCase__			:				Union[str, Any]												=       model_output.device
   UpperCAmelCase__			:				Tuple												=       randn_tensor(model_output.shape ,							dtype=model_output.dtype ,							device=snake_case__ ,							generator=snake_case__   )

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
     UpperCAmelCase__			:				Optional[Any]												=       sigma_hat if self.state_in_first_order else sigma_interpol
     UpperCAmelCase__			:				List[Any]												=       sample - sigma_input * model_output
   elif self.config.prediction_type == "v_prediction":
     UpperCAmelCase__			:				Tuple												=       sigma_hat if self.state_in_first_order else sigma_interpol
     UpperCAmelCase__			:				List[Any]												=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
         sample / (sigma_input**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`"""   )

   if self.state_in_first_order:
     # 2. Convert to an ODE derivative for 1st order
     UpperCAmelCase__			:				str												=       (sample - pred_original_sample) / sigma_hat
     # 3. delta timestep
     UpperCAmelCase__			:				List[Any]												=       sigma_interpol - sigma_hat

     # store for 2nd order step
     UpperCAmelCase__			:				int												=       sample
     UpperCAmelCase__			:				int												=       dt
     UpperCAmelCase__			:				List[str]												=       sample + derivative * dt
   else:
     # DPM-Solver-2
     # 2. Convert to an ODE derivative for 2nd order
     UpperCAmelCase__			:				int												=       (sample - pred_original_sample) / sigma_interpol
     # 3. delta timestep
     UpperCAmelCase__			:				List[str]												=       sigma_down - sigma_hat

     UpperCAmelCase__			:				Optional[Any]												=       self.sample
     UpperCAmelCase__			:				Optional[int]												=       None

     UpperCAmelCase__			:				int												=       sample + derivative * dt
     UpperCAmelCase__			:				int												=       prev_sample + noise * sigma_up

   if not return_dict:
     return (prev_sample,)

   return SchedulerOutput(prev_sample=snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[Any] ,							snake_case__		:     torch.FloatTensor ,							snake_case__		:     torch.FloatTensor ,							snake_case__		:     torch.FloatTensor ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       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
     UpperCAmelCase__			:				List[str]												=       self.timesteps.to(original_samples.device ,							dtype=torch.floataa   )
     UpperCAmelCase__			:				Optional[int]												=       timesteps.to(original_samples.device ,							dtype=torch.floataa   )
   else:
     UpperCAmelCase__			:				Optional[int]												=       self.timesteps.to(original_samples.device   )
     UpperCAmelCase__			:				Union[str, Any]												=       timesteps.to(original_samples.device   )

   UpperCAmelCase__			:				List[Any]												=       [self.index_for_timestep(snake_case__ ,							snake_case__   ) for t in timesteps]

   UpperCAmelCase__			:				List[str]												=       sigmas[step_indices].flatten()
   while len(sigma.shape   ) < len(original_samples.shape   ):
     UpperCAmelCase__			:				Union[str, Any]												=       sigma.unsqueeze(-1   )

   UpperCAmelCase__			:				List[str]												=       original_samples + noise * sigma
   return noisy_samples







 def __len__(				self		:     Any   ):



   '''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    =					{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DonutSwinModel""",
          """DonutSwinPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					["""DonutFeatureExtractor"""]
      SCREAMING_SNAKE_CASE    =					["""DonutImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
      from .processing_donut import DonutProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_donut_swin import (
                DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                DonutSwinModel,
                DonutSwinPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_donut import DonutFeatureExtractor
            from .image_processing_donut import DonutImageProcessor

else:
      import sys

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



