





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    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=0.9_9_9	,					_UpperCAmelCase="cosine"	,					)  ->     Tuple:


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(_UpperCAmelCase ):
												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(_UpperCAmelCase ):
												return math.exp(t * -1_2.0 )

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

				lowerCamelCase		       =[]
				for i in range(_SCREAMING_SNAKE_CASE ):
								lowerCamelCase		       =i / num_diffusion_timesteps
								lowerCamelCase		       =(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE )	,					_SCREAMING_SNAKE_CASE ) )
				return torch.tensor(_SCREAMING_SNAKE_CASE	,					dtype=torch.floataa )




class 					__A   (  lowerCAmelCase__			,    lowerCAmelCase__			):
				__A            = [e.name for e in KarrasDiffusionSchedulers]
				__A            = 2


				@register_to_config
				def __init__(						self      ,       UpperCAmelCase_ = 1000      ,       UpperCAmelCase_ = 0.0_0_0_8_5      ,       UpperCAmelCase_ = 0.0_1_2      ,       UpperCAmelCase_ = "linear"      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = "epsilon"      ,       UpperCAmelCase_ = "linspace"      ,       UpperCAmelCase_ = 0      ,       ):
								if trained_betas is not None:
												lowerCamelCase		       =torch.tensor(_A      ,       dtype=torch.floataa							)
								elif beta_schedule == "linear":
												lowerCamelCase		       =torch.linspace(_A      ,       _A      ,       _A      ,       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      ,       _A      ,       dtype=torch.floataa							) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												lowerCamelCase		       =betas_for_alpha_bar(_A							)
								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							)

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


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=None							):
								if schedule_timesteps is None:
												lowerCamelCase		       =self.timesteps

								lowerCamelCase		       =(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:
												lowerCamelCase		       =1 if len(_A							) > 1 else 0
								else:
												lowerCamelCase		       =timestep.cpu().item() if torch.is_tensor(_A							) else timestep
												lowerCamelCase		       =self._index_counter[timestep_int]

								return indices[pos].item()


				@property
				def _snake_case				(						self							):
								# 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 _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       ):
								lowerCamelCase		       =self.index_for_timestep(_A							)

								if self.state_in_first_order:
												lowerCamelCase		       =self.sigmas[step_index]
								else:
												lowerCamelCase		       =self.sigmas_interpol[step_index - 1]

								lowerCamelCase		       =sample / ((sigma**2 + 1) ** 0.5)
								return sample


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       ):
								lowerCamelCase		       =num_inference_steps

								lowerCamelCase		       =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":
												lowerCamelCase		       =np.linspace(0      ,       num_train_timesteps - 1      ,       _A      ,       dtype=_A							)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
												lowerCamelCase		       =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      ,       _A							) * step_ratio).round()[::-1].copy().astype(_A							)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												lowerCamelCase		       =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(_A      ,       0      ,       -step_ratio							)).round().copy().astype(_A							)
												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		       =torch.from_numpy(np.log(_A							)							).to(_A							)

								lowerCamelCase		       =np.interp(_A      ,       np.arange(0      ,       len(_A							)							)      ,       _A							)
								lowerCamelCase		       =np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
								lowerCamelCase		       =torch.from_numpy(_A							).to(device=_A							)

								# compute up and down sigmas
								lowerCamelCase		       =sigmas.roll(-1							)
								lowerCamelCase		       =0.0
								lowerCamelCase		       =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
								lowerCamelCase		       =(sigmas_next**2 - sigmas_up**2) ** 0.5
								lowerCamelCase		       =0.0

								# compute interpolated sigmas
								lowerCamelCase		       =sigmas.log().lerp(sigmas_down.log()      ,       0.5							).exp()
								lowerCamelCase		       =0.0

								# set sigmas
								lowerCamelCase		       =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
								lowerCamelCase		       =torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)
								lowerCamelCase		       =torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2							), sigmas_up[-1:]]							)
								lowerCamelCase		       =torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2							), sigmas_down[-1:]]							)

								if str(_A							).startswith("""mps"""							):
												# mps does not support float64
												lowerCamelCase		       =torch.from_numpy(_A							).to(_A      ,       dtype=torch.floataa							)
								else:
												lowerCamelCase		       =torch.from_numpy(_A							).to(_A							)

								lowerCamelCase		       =self.sigma_to_t(_A							).to(_A      ,       dtype=timesteps.dtype							)
								lowerCamelCase		       =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,       dim=-1							).flatten()

								lowerCamelCase		       =torch.cat([timesteps[:1], interleaved_timesteps]							)

								lowerCamelCase		       =None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								lowerCamelCase		       =defaultdict(_A							)


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								# get log sigma
								lowerCamelCase		       =sigma.log()

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

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

								lowerCamelCase		       =self.log_sigmas[low_idx]
								lowerCamelCase		       =self.log_sigmas[high_idx]

								# interpolate sigmas
								lowerCamelCase		       =(low - log_sigma) / (low - high)
								lowerCamelCase		       =w.clamp(0      ,       1							)

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


				@property
				def _snake_case				(						self							):
								return self.sample is None


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       ):
								lowerCamelCase		       =self.index_for_timestep(_A							)

								# advance index counter by 1
								lowerCamelCase		       =timestep.cpu().item() if torch.is_tensor(_A							) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
												lowerCamelCase		       =self.sigmas[step_index]
												lowerCamelCase		       =self.sigmas_interpol[step_index]
												lowerCamelCase		       =self.sigmas_up[step_index]
												lowerCamelCase		       =self.sigmas_down[step_index - 1]
								else:
												# 2nd order / KPDM2's method
												lowerCamelCase		       =self.sigmas[step_index - 1]
												lowerCamelCase		       =self.sigmas_interpol[step_index - 1]
												lowerCamelCase		       =self.sigmas_up[step_index - 1]
												lowerCamelCase		       =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
								lowerCamelCase		       =0
								lowerCamelCase		       =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								lowerCamelCase		       =model_output.device
								lowerCamelCase		       =randn_tensor(model_output.shape      ,       dtype=model_output.dtype      ,       device=_A      ,       generator=_A							)

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												lowerCamelCase		       =sigma_hat if self.state_in_first_order else sigma_interpol
												lowerCamelCase		       =sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
												lowerCamelCase		       =sigma_hat if self.state_in_first_order else sigma_interpol
												lowerCamelCase		       =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
												lowerCamelCase		       =(sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												lowerCamelCase		       =sigma_interpol - sigma_hat

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

												lowerCamelCase		       =self.sample
												lowerCamelCase		       =None

												lowerCamelCase		       =sample + derivative * dt
												lowerCamelCase		       =prev_sample + noise * sigma_up

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=_A							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCamelCase		       =self.sigmas.to(device=original_samples.device      ,       dtype=original_samples.dtype							)
								if original_samples.device.type == "mps" and torch.is_floating_point(_A							):
												# 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		       =[self.index_for_timestep(_A      ,       _A							) 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							):
								return self.config.num_train_timesteps








from typing import TYPE_CHECKING

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


UpperCamelCase__     :					Dict								=			{
    "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:
       UpperCamelCase__     :					Optional[int]								=			[
           "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DonutSwinModel",
           "DonutSwinPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					List[Any]								=			["DonutFeatureExtractor"]
       UpperCamelCase__     :					Dict								=			["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

       UpperCamelCase__     :					Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


