

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     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Any=0.999       ,      _SCREAMING_SNAKE_CASE    :						str="cosine"       ,      ):





    """simple docstring"""


    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_SCREAMING_SNAKE_CASE    :						List[str]				):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

    elif alpha_transform_type == "exp":

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

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

    SCREAMING_SNAKE_CASE_													=  []
    for i in range(_SCREAMING_SNAKE_CASE				):
        SCREAMING_SNAKE_CASE_													=  i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE_													=  (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 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__				):
  __lowerCAmelCase   :		int         =     [e.name for e in KarrasDiffusionSchedulers]
  __lowerCAmelCase   :		Optional[int]         =     2

  @register_to_config
  def __init__(    self  ,						_A = 1000  ,						_A = 0.0_0_0_8_5  ,						_A = 0.0_1_2  ,						_A = "linear"  ,						_A = None  ,						_A = "epsilon"  ,						_A = "linspace"  ,						_A = 0  ,						):
      if trained_betas is not None:
          SCREAMING_SNAKE_CASE_													=  torch.tensor(_A  ,						dtype=torch.floataa)
      elif beta_schedule == "linear":
          SCREAMING_SNAKE_CASE_													=  torch.linspace(_A  ,						_A  ,						_A  ,						dtype=torch.floataa)
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          SCREAMING_SNAKE_CASE_													=  (
              torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						_A  ,						dtype=torch.floataa) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          SCREAMING_SNAKE_CASE_													=  betas_for_alpha_bar(_A)
      else:
          raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

      SCREAMING_SNAKE_CASE_													=  1.0 - self.betas
      SCREAMING_SNAKE_CASE_													=  torch.cumprod(self.alphas  ,						dim=0)

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

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A=None):
      if schedule_timesteps is None:
          SCREAMING_SNAKE_CASE_													=  self.timesteps

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

      return indices[pos].item()

  @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  ,						_A  ,						_A  ,						):
      SCREAMING_SNAKE_CASE_													=  self.index_for_timestep(_A)

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

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

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A = None  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  num_inference_steps

      SCREAMING_SNAKE_CASE_													=  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":
          SCREAMING_SNAKE_CASE_													=  np.linspace(0  ,						num_train_timesteps - 1  ,						_A  ,						dtype=_A)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
          SCREAMING_SNAKE_CASE_													=  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
          SCREAMING_SNAKE_CASE_													=  (np.arange(0  ,						_A) * step_ratio).round()[::-1].copy().astype(_A)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          SCREAMING_SNAKE_CASE_													=  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
          SCREAMING_SNAKE_CASE_													=  (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'.""")

      SCREAMING_SNAKE_CASE_													=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
      SCREAMING_SNAKE_CASE_													=  torch.from_numpy(np.log(_A)).to(_A)

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

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

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

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

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

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

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

      SCREAMING_SNAKE_CASE_													=  None

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

  def   lowerCAmelCase__   (    self  ,						_A):
      # get log sigma
      SCREAMING_SNAKE_CASE_													=  sigma.log()

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

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

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

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

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

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

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A  ,						_A = None  ,						_A = True  ,						):
      SCREAMING_SNAKE_CASE_													=  self.index_for_timestep(_A)

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

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

      SCREAMING_SNAKE_CASE_													=  model_output.device
      SCREAMING_SNAKE_CASE_													=  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":
          SCREAMING_SNAKE_CASE_													=  sigma_hat if self.state_in_first_order else sigma_interpol
          SCREAMING_SNAKE_CASE_													=  sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
          SCREAMING_SNAKE_CASE_													=  sigma_hat if self.state_in_first_order else sigma_interpol
          SCREAMING_SNAKE_CASE_													=  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
          SCREAMING_SNAKE_CASE_													=  (sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          SCREAMING_SNAKE_CASE_													=  sigma_interpol - sigma_hat

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

          SCREAMING_SNAKE_CASE_													=  self.sample
          SCREAMING_SNAKE_CASE_													=  None

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

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=_A)

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A  ,						):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      SCREAMING_SNAKE_CASE_													=  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
          SCREAMING_SNAKE_CASE_													=  self.timesteps.to(original_samples.device  ,						dtype=torch.floataa)
          SCREAMING_SNAKE_CASE_													=  timesteps.to(original_samples.device  ,						dtype=torch.floataa)
      else:
          SCREAMING_SNAKE_CASE_													=  self.timesteps.to(original_samples.device)
          SCREAMING_SNAKE_CASE_													=  timesteps.to(original_samples.device)

      SCREAMING_SNAKE_CASE_													=  [self.index_for_timestep(_A  ,						_A) for t in timesteps]

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

      SCREAMING_SNAKE_CASE_													=  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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

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


