

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 _a (       SCREAMING_SNAKE_CASE		:			Tuple    ,					SCREAMING_SNAKE_CASE		:			str=0.9_9_9    ,					SCREAMING_SNAKE_CASE		:			Any="cosine"    ,					)    ->    Union[str, Any]:

 """simple docstring"""






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(SCREAMING_SNAKE_CASE		:			Optional[Any]		):
   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(SCREAMING_SNAKE_CASE		:			Union[str, Any]		):
   return math.exp(t * -1_2.0		)

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

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






class 							A_ (		snake_case__							,	snake_case__					):
     _lowercase			:     int								=	[e.name for e in KarrasDiffusionSchedulers]
     _lowercase			:     List[str]								=	2


     @register_to_config
     def __init__(							self			:		Optional[Any]			,  UpperCAmelCase			:		List[Any] = 1_0_0_0			,  UpperCAmelCase			:		Optional[int] = 0.00085			,  UpperCAmelCase			:		int = 0.012			,  UpperCAmelCase			:		Union[str, Any] = "linear"			,  UpperCAmelCase			:		int = None			,  UpperCAmelCase			:		List[str] = "epsilon"			,  UpperCAmelCase			:		int = False			,  UpperCAmelCase			:		List[str] = False			,  UpperCAmelCase			:		Optional[int] = 1.0			,  UpperCAmelCase			:		Any = "linspace"			,  UpperCAmelCase			:		List[Any] = 0			,  )							->	List[str]:
      if trained_betas is not None:
       __lowerCAmelCase:   Tuple   	=      torch.tensor(UpperCAmelCase			,  dtype=torch.floataa					)
      elif beta_schedule == "linear":
       __lowerCAmelCase:   List[str]   	=      torch.linspace(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  dtype=torch.floataa					)
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowerCAmelCase:   str   	=      (
           torch.linspace(beta_start**0.5			,  beta_end**0.5			,  UpperCAmelCase			,  dtype=torch.floataa					) ** 2
       )
      elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowerCAmelCase:   List[str]   	=      betas_for_alpha_bar(UpperCAmelCase			,  alpha_transform_type='cosine'					)
      elif beta_schedule == "exp":
       __lowerCAmelCase:   List[Any]   	=      betas_for_alpha_bar(UpperCAmelCase			,  alpha_transform_type='exp'					)
      else:
       raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

      #  set all values
      self.set_timesteps(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase					)
      __lowerCAmelCase:   Optional[Any]   	=      use_karras_sigmas


     def   UpperCAmelCase  (							self			:		Union[str, Any]			,  UpperCAmelCase			:		str			,  UpperCAmelCase			:		List[str]=None					)							->	Dict:
      if schedule_timesteps is None:
       __lowerCAmelCase:   Dict   	=      self.timesteps

      __lowerCAmelCase:   Tuple   	=      (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:   Optional[int]   	=      1 if len(UpperCAmelCase					) > 1 else 0
      else:
       __lowerCAmelCase:   Dict   	=      timestep.cpu().item() if torch.is_tensor(UpperCAmelCase					) else timestep
       __lowerCAmelCase:   List[str]   	=      self._index_counter[timestep_int]

      return indices[pos].item()


     @property
     def   UpperCAmelCase  (							self			:		Dict					)							->	str:
      # 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   UpperCAmelCase  (							self			:		Any			,  UpperCAmelCase			:		Tuple			,  UpperCAmelCase			:		Optional[Any]			,  )							->	torch.FloatTensor:
      __lowerCAmelCase:   List[str]   	=      self.index_for_timestep(UpperCAmelCase					)

      __lowerCAmelCase:   Dict   	=      self.sigmas[step_index]
      __lowerCAmelCase:   str   	=      sample / ((sigma**2 + 1) ** 0.5)
      return sample


     def   UpperCAmelCase  (							self			:		Optional[Any]			,  UpperCAmelCase			:		Optional[int]			,  UpperCAmelCase			:		Any = None			,  UpperCAmelCase			:		Union[str, Any] = None			,  )							->	int:
      __lowerCAmelCase:   Union[str, Any]   	=      num_inference_steps

      __lowerCAmelCase:   List[str]   	=      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:   List[str]   	=      np.linspace(0			,  num_train_timesteps - 1			,  UpperCAmelCase			,  dtype=UpperCAmelCase					)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
       __lowerCAmelCase:   Tuple   	=      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:   List[str]   	=      (np.arange(0			,  UpperCAmelCase					) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase					)
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       __lowerCAmelCase:   List[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
       __lowerCAmelCase:   Any   	=      (np.arange(UpperCAmelCase			,  0			,  -step_ratio					)).round().copy().astype(UpperCAmelCase					)
       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:   str   	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
      __lowerCAmelCase:   Union[str, Any]   	=      np.log(UpperCAmelCase					)
      __lowerCAmelCase:   Optional[Any]   	=      np.interp(UpperCAmelCase			,  np.arange(0			,  len(UpperCAmelCase					)					)			,  UpperCAmelCase					)

      if self.config.use_karras_sigmas:
       __lowerCAmelCase:   Any   	=      self._convert_to_karras(in_sigmas=UpperCAmelCase			,  num_inference_steps=self.num_inference_steps					)
       __lowerCAmelCase:   List[Any]   	=      np.array([self._sigma_to_t(UpperCAmelCase			,  UpperCAmelCase					) for sigma in sigmas]					)

      __lowerCAmelCase:   List[str]   	=      np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
      __lowerCAmelCase:   List[Any]   	=      torch.from_numpy(UpperCAmelCase					).to(device=UpperCAmelCase					)
      __lowerCAmelCase:   List[Any]   	=      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

      __lowerCAmelCase:   List[Any]   	=      torch.from_numpy(UpperCAmelCase					)
      __lowerCAmelCase:   Optional[int]   	=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

      if str(UpperCAmelCase					).startswith('mps'					):
       # mps does not support float64
       __lowerCAmelCase:   str   	=      timesteps.to(UpperCAmelCase			,  dtype=torch.floataa					)
      else:
       __lowerCAmelCase:   str   	=      timesteps.to(device=UpperCAmelCase					)

      # empty dt and derivative
      __lowerCAmelCase:   Optional[int]   	=      None
      __lowerCAmelCase:   Dict   	=      None

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


     def   UpperCAmelCase  (							self			:		Union[str, Any]			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Any					)							->	Optional[int]:
      # get log sigma
      __lowerCAmelCase:   Optional[int]   	=      np.log(UpperCAmelCase					)

      # get distribution
      __lowerCAmelCase:   Optional[int]   	=      log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      __lowerCAmelCase:   Dict   	=      np.cumsum((dists >= 0)			,  axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
      __lowerCAmelCase:   Dict   	=      low_idx + 1

      __lowerCAmelCase:   str   	=      log_sigmas[low_idx]
      __lowerCAmelCase:   str   	=      log_sigmas[high_idx]

      # interpolate sigmas
      __lowerCAmelCase:   int   	=      (low - log_sigma) / (low - high)
      __lowerCAmelCase:   Dict   	=      np.clip(UpperCAmelCase			,  0			,  1					)

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


     def   UpperCAmelCase  (							self			:		List[str]			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Tuple					)							->	torch.FloatTensor:
      __lowerCAmelCase:   int   	=      in_sigmas[-1].item()
      __lowerCAmelCase:   Optional[Any]   	=      in_sigmas[0].item()

      __lowerCAmelCase:   str   	=      7.0  # 7.0 is the value used in the paper
      __lowerCAmelCase:   List[Any]   	=      np.linspace(0			,  1			,  UpperCAmelCase					)
      __lowerCAmelCase:   Optional[Any]   	=      sigma_min ** (1 / rho)
      __lowerCAmelCase:   int   	=      sigma_max ** (1 / rho)
      __lowerCAmelCase:   Union[str, Any]   	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas


     @property
     def   UpperCAmelCase  (							self			:		Tuple					)							->	List[Any]:
      return self.dt is None


     def   UpperCAmelCase  (							self			:		str			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Optional[int]			,  UpperCAmelCase			:		List[Any]			,  UpperCAmelCase			:		int = True			,  )							->	Union[SchedulerOutput, Tuple]:
      __lowerCAmelCase:   Any   	=      self.index_for_timestep(UpperCAmelCase					)

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

      if self.state_in_first_order:
       __lowerCAmelCase:   List[str]   	=      self.sigmas[step_index]
       __lowerCAmelCase:   Optional[Any]   	=      self.sigmas[step_index + 1]
      else:
       # 2nd order / Heun's method
       __lowerCAmelCase:   List[Any]   	=      self.sigmas[step_index - 1]
       __lowerCAmelCase:   Tuple   	=      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
      __lowerCAmelCase:   Dict   	=      0
      __lowerCAmelCase:   Any   	=      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":
       __lowerCAmelCase:   Optional[Any]   	=      sigma_hat if self.state_in_first_order else sigma_next
       __lowerCAmelCase:   Tuple   	=      sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
       __lowerCAmelCase:   Tuple   	=      sigma_hat if self.state_in_first_order else sigma_next
       __lowerCAmelCase:   str   	=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
      elif self.config.prediction_type == "sample":
       __lowerCAmelCase:   int   	=      model_output
      else:
       raise ValueError(
           F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

      if self.config.clip_sample:
       __lowerCAmelCase:   Dict   	=      pred_original_sample.clamp(
           -self.config.clip_sample_range			,  self.config.clip_sample_range					)

      if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       __lowerCAmelCase:   int   	=      (sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       __lowerCAmelCase:   int   	=      sigma_next - sigma_hat

       # store for 2nd order step
       __lowerCAmelCase:   Any   	=      derivative
       __lowerCAmelCase:   List[Any]   	=      dt
       __lowerCAmelCase:   Tuple   	=      sample
      else:
       # 2. 2nd order / Heun's method
       __lowerCAmelCase:   str   	=      (sample - pred_original_sample) / sigma_next
       __lowerCAmelCase:   Tuple   	=      (self.prev_derivative + derivative) / 2

       # 3. take prev timestep & sample
       __lowerCAmelCase:   Union[str, Any]   	=      self.dt
       __lowerCAmelCase:   List[str]   	=      self.sample

       # free dt and derivative
       # Note, this puts the scheduler in "first order mode"
       __lowerCAmelCase:   List[Any]   	=      None
       __lowerCAmelCase:   List[Any]   	=      None
       __lowerCAmelCase:   Optional[int]   	=      None

      __lowerCAmelCase:   Dict   	=      sample + derivative * dt

      if not return_dict:
       return (prev_sample,)

      return SchedulerOutput(prev_sample=UpperCAmelCase					)


     def   UpperCAmelCase  (							self			:		str			,  UpperCAmelCase			:		Any			,  UpperCAmelCase			:		Optional[int]			,  UpperCAmelCase			:		List[Any]			,  )							->	torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      __lowerCAmelCase:   Union[str, Any]   	=      self.sigmas.to(device=original_samples.device			,  dtype=original_samples.dtype					)
      if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase					):
       # mps does not support float64
       __lowerCAmelCase:   str   	=      self.timesteps.to(original_samples.device			,  dtype=torch.floataa					)
       __lowerCAmelCase:   int   	=      timesteps.to(original_samples.device			,  dtype=torch.floataa					)
      else:
       __lowerCAmelCase:   List[str]   	=      self.timesteps.to(original_samples.device					)
       __lowerCAmelCase:   Optional[int]   	=      timesteps.to(original_samples.device					)

      __lowerCAmelCase:   Tuple   	=      [self.index_for_timestep(UpperCAmelCase			,  UpperCAmelCase					) for t in timesteps]

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

      __lowerCAmelCase:   Optional[Any]   	=      original_samples + noise * sigma
      return noisy_samples


     def __len__(							self			:		Optional[int]					)							->	Tuple:
      return self.config.num_train_timesteps









from typing import TYPE_CHECKING

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


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

