
"""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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			torch.FloatTensor
    __lowercase				:			Optional[torch.FloatTensor]	=       None


def 					a__ ( a :					List[Any]							,  a :					int=0.999							,  a :					List[Any]="cosine"							,  ):

  """simple docstring"""







  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(a :					List[str]		):
      return math.exp(t * -12.0		)

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

  _snake_case  :    Optional[int]            = []
  for i in range(a		):
    _snake_case  :    Union[str, Any]            = i / num_diffusion_timesteps
    _snake_case  :    int            = (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   _UpperCAmelCase					(       _snake_case		,       _snake_case):
    __lowercase				:			List[str]	=       [e.name for e in KarrasDiffusionSchedulers]
    __lowercase				:			Optional[int]	=       1
    @register_to_config
    def __init__(     self    ,     snake_case_ = 10_00    ,     snake_case_ = 0.0001    ,     snake_case_ = 0.02    ,     snake_case_ = "linear"    ,     snake_case_ = None    ,     snake_case_ = "epsilon"    ,     snake_case_ = "linspace"    ,     snake_case_ = 0    ,     ):
      if trained_betas is not None:
        _snake_case  :    List[Any]            = torch.tensor(snake_case_    ,     dtype=torch.floataa  )
      elif beta_schedule == "linear":
        _snake_case  :    str            = 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.
        _snake_case  :    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
        _snake_case  :    Any            = betas_for_alpha_bar(snake_case_  )
      else:
        raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'  )

      _snake_case  :    Optional[int]            = 1.0 - self.betas
      _snake_case  :    Union[str, Any]            = torch.cumprod(self.alphas    ,     dim=0  )

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

      # setable values
      _snake_case  :    int            = None
      _snake_case  :    int            = np.linspace(0    ,     num_train_timesteps - 1    ,     snake_case_    ,     dtype=snake_case_  )[::-1].copy()
      _snake_case  :    Any            = torch.from_numpy(snake_case_  )
      _snake_case  :    Dict            = False
    @property
    def        lowerCamelCase__						(     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        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      if isinstance(snake_case_    ,     torch.Tensor  ):
        _snake_case  :    Optional[int]            = timestep.to(self.timesteps.device  )
      _snake_case  :    Optional[int]            = (self.timesteps == timestep).nonzero().item()
      _snake_case  :    Any            = self.sigmas[step_index]
      _snake_case  :    Tuple            = sample / ((sigma**2 + 1) ** 0.5)
      _snake_case  :    int            = True
      return sample
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    int            = 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":
        _snake_case  :    List[Any]            = np.linspace(0    ,     self.config.num_train_timesteps - 1    ,     snake_case_    ,     dtype=snake_case_  )[
            ::-1
        ].copy()
      elif self.config.timestep_spacing == "leading":
        _snake_case  :    Dict            = 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
        _snake_case  :    Any            = (np.arange(0    ,     snake_case_  ) * step_ratio).round()[::-1].copy().astype(snake_case_  )
        timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
        _snake_case  :    Union[str, Any]            = 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
        _snake_case  :    Optional[Any]            = (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\'.'  )

      _snake_case  :    List[Any]            = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
      _snake_case  :    Tuple            = np.interp(snake_case_    ,     np.arange(0    ,     len(snake_case_  )  )    ,     snake_case_  )
      _snake_case  :    Any            = np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
      _snake_case  :    Union[str, Any]            = torch.from_numpy(snake_case_  ).to(device=snake_case_  )
      if str(snake_case_  ).startswith("mps"  ):
        # mps does not support float64
        _snake_case  :    Dict            = torch.from_numpy(snake_case_  ).to(snake_case_    ,     dtype=torch.floataa  )
      else:
        _snake_case  :    Union[str, Any]            = torch.from_numpy(snake_case_  ).to(device=snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = True    ,     ):

      if (
          isinstance(snake_case_    ,     snake_case_  )
          or isinstance(snake_case_    ,     torch.IntTensor  )
          or isinstance(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(snake_case_    ,     torch.Tensor  ):
        _snake_case  :    Any            = timestep.to(self.timesteps.device  )

      _snake_case  :    List[str]            = (self.timesteps == timestep).nonzero().item()
      _snake_case  :    List[Any]            = self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        _snake_case  :    Dict            = sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        _snake_case  :    Dict            = 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`'  )

      _snake_case  :    List[str]            = self.sigmas[step_index]
      _snake_case  :    Any            = self.sigmas[step_index + 1]
      _snake_case  :    Union[str, Any]            = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      _snake_case  :    Optional[Any]            = (sigma_to**2 - sigma_up**2) ** 0.5

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

      _snake_case  :    Optional[int]            = sigma_down - sigma

      _snake_case  :    Dict            = sample + derivative * dt

      _snake_case  :    Any            = model_output.device
      _snake_case  :    List[Any]            = randn_tensor(model_output.shape    ,     dtype=model_output.dtype    ,     device=snake_case_    ,     generator=snake_case_  )

      _snake_case  :    Union[str, Any]            = prev_sample + noise * sigma_up

      if not return_dict:
        return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=snake_case_    ,     pred_original_sample=snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     ):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      _snake_case  :    str            = 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
        _snake_case  :    int            = self.timesteps.to(original_samples.device    ,     dtype=torch.floataa  )
        _snake_case  :    Dict            = timesteps.to(original_samples.device    ,     dtype=torch.floataa  )
      else:
        _snake_case  :    Any            = self.timesteps.to(original_samples.device  )
        _snake_case  :    List[str]            = timesteps.to(original_samples.device  )

      _snake_case  :    Dict            = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

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


    def __len__(     self  ):
      return self.config.num_train_timesteps


'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase       :     Tuple			=    {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

