






"""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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def     __lowercase					(    a :				int       ,		a :				str=0.9_9_9       ,		a :				Tuple="cosine"       ,		)					->					List[Any]:
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(a :				Optional[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(a :				Tuple ):
                  return math.exp(t * -1_2.0 )

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

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


class      _lowercase (     lowerCAmelCase		,  lowerCAmelCase						):
      _a   :     Tuple								=   [e.name for e in KarrasDiffusionSchedulers]
      _a   :     Dict								=   2


      @register_to_config
      def __init__(    self				:	Tuple							,   a				:	int = 1_0_0_0							,   a				:	float = 0.0_0_0_8_5							,   a				:	float = 0.0_1_2							,   a				:	str = "linear"							,   a				:	Optional[Union[np.ndarray, List[float]]] = None							,   a				:	str = "epsilon"							,   a				:	str = "linspace"							,   a				:	int = 0							,   ):





            """simple docstring"""







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

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

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


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	List[str]							,   a				:	List[str]=None						):





            """simple docstring"""







            if schedule_timesteps is None:
                  __snake_case      :    str  			=self.timesteps

            __snake_case      :    Union[str, Any]  			=(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:
                  __snake_case      :    int  			=1 if len(a						) > 1 else 0
            else:
                  __snake_case      :    int  			=timestep.cpu().item() if torch.is_tensor(a						) else timestep
                  __snake_case      :    List[Any]  			=self._index_counter[timestep_int]

            return indices[pos].item()


      @property
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """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				:	str							,   a				:	torch.FloatTensor							,   a				:	Union[float, torch.FloatTensor]							,   ):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.index_for_timestep(a						)

            if self.state_in_first_order:
                  __snake_case      :    Union[str, Any]  			=self.sigmas[step_index]
            else:
                  __snake_case      :    Optional[Any]  			=self.sigmas_interpol[step_index]

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


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	int							,   a				:	Union[str, torch.device] = None							,   a				:	Optional[int] = None							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=num_inference_steps

            __snake_case      :    Union[str, Any]  			=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":
                  __snake_case      :    Any  			=np.linspace(0							,   num_train_timesteps - 1							,   a							,   dtype=a						)[::-1].copy()
            elif self.config.timestep_spacing == "leading":
                  __snake_case      :    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
                  __snake_case      :    str  			=(np.arange(0							,   a						) * step_ratio).round()[::-1].copy().astype(a						)
                  timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                  __snake_case      :    Optional[Any]  			=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
                  __snake_case      :    List[Any]  			=(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\'.'''						)

            __snake_case      :    Union[str, Any]  			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
            __snake_case      :    List[str]  			=torch.from_numpy(np.log(a						)						).to(a						)

            __snake_case      :    Optional[int]  			=np.interp(a							,   np.arange(0							,   len(a						)						)							,   a						)
            __snake_case      :    str  			=np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
            __snake_case      :    List[Any]  			=torch.from_numpy(a						).to(device=a						)

            # interpolate sigmas
            __snake_case      :    Tuple  			=sigmas.log().lerp(sigmas.roll(1						).log()							,   0.5						).exp()

            __snake_case      :    Union[str, Any]  			=torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
            __snake_case      :    Optional[int]  			=torch.cat(
                [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)

            if str(a						).startswith('''mps'''						):
                  # mps does not support float64
                  __snake_case      :    Optional[int]  			=torch.from_numpy(a						).to(a							,   dtype=torch.floataa						)
            else:
                  __snake_case      :    Dict  			=torch.from_numpy(a						).to(a						)

            # interpolate timesteps
            __snake_case      :    Dict  			=self.sigma_to_t(a						).to(a							,   dtype=timesteps.dtype						)
            __snake_case      :    List[str]  			=torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])							,   dim=-1						).flatten()

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

            __snake_case      :    str  			=None

            # for exp beta schedules, such as the one for `pipeline_shap_e.py`
            # we need an index counter
            __snake_case      :    Dict  			=defaultdict(a						)


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    str  			=sigma.log()

            # get distribution
            __snake_case      :    Optional[Any]  			=log_sigma - self.log_sigmas[:, None]

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

            __snake_case      :    int  			=self.log_sigmas[low_idx]
            __snake_case      :    Any  			=self.log_sigmas[high_idx]

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

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


      @property
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            return self.sample is None


      def _UpperCamelCase						(    self				:	Dict							,   a				:	Union[torch.FloatTensor, np.ndarray]							,   a				:	Union[float, torch.FloatTensor]							,   a				:	Union[torch.FloatTensor, np.ndarray]							,   a				:	bool = True							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.index_for_timestep(a						)

            # advance index counter by 1
            __snake_case      :    List[Any]  			=timestep.cpu().item() if torch.is_tensor(a						) else timestep
            self._index_counter[timestep_int] += 1

            if self.state_in_first_order:
                  __snake_case      :    List[Any]  			=self.sigmas[step_index]
                  __snake_case      :    Dict  			=self.sigmas_interpol[step_index + 1]
                  __snake_case      :    Dict  			=self.sigmas[step_index + 1]
            else:
                  # 2nd order / KDPM2's method
                  __snake_case      :    str  			=self.sigmas[step_index - 1]
                  __snake_case      :    Tuple  			=self.sigmas_interpol[step_index]
                  __snake_case      :    Any  			=self.sigmas[step_index]

            # 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
            __snake_case      :    int  			=0
            __snake_case      :    Optional[int]  			=sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
            if self.config.prediction_type == "epsilon":
                  __snake_case      :    List[str]  			=sigma_hat if self.state_in_first_order else sigma_interpol
                  __snake_case      :    Optional[Any]  			=sample - sigma_input * model_output
            elif self.config.prediction_type == "v_prediction":
                  __snake_case      :    Optional[int]  			=sigma_hat if self.state_in_first_order else sigma_interpol
                  __snake_case      :    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
                  __snake_case      :    Optional[Any]  			=(sample - pred_original_sample) / sigma_hat
                  # 3. delta timestep
                  __snake_case      :    Union[str, Any]  			=sigma_interpol - sigma_hat

                  # store for 2nd order step
                  __snake_case      :    Tuple  			=sample
            else:
                  # DPM-Solver-2
                  # 2. Convert to an ODE derivative for 2nd order
                  __snake_case      :    Tuple  			=(sample - pred_original_sample) / sigma_interpol

                  # 3. delta timestep
                  __snake_case      :    str  			=sigma_next - sigma_hat

                  __snake_case      :    str  			=self.sample
                  __snake_case      :    List[Any]  			=None

            __snake_case      :    Tuple  			=sample + derivative * dt

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=a						)


      def _UpperCamelCase						(    self				:	List[Any]							,   a				:	torch.FloatTensor							,   a				:	torch.FloatTensor							,   a				:	torch.FloatTensor							,   ):





            """simple docstring"""







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

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

            __snake_case      :    Tuple  			=sigmas[step_indices].flatten()
            while len(sigma.shape						) < len(original_samples.shape						):
                  __snake_case      :    List[Any]  			=sigma.unsqueeze(-1						)

            __snake_case      :    List[Any]  			=original_samples + noise * sigma
            return noisy_samples







      def __len__(    self				:	str						):





            """simple docstring"""







            return self.config.num_train_timesteps










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_	:				str										= {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Union[str, Any]										= ["""LayoutXLMTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


