"""simple docstring"""


import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


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






def     __UpperCAmelCase					(			lowercase   ,lowercase=0.9_99   ,lowercase="cosine"   ,):







  """simple docstring"""
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase		):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

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

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

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


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_   ):


    @register_to_config
    def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  int = 1000   ,				__lowerCAmelCase	:  str = "fixed_small_log"   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Optional[float] = 1.0   ,				__lowerCAmelCase	:  str = "epsilon"   ,				__lowerCAmelCase	:  str = "squaredcos_cap_v2"   ,				):
      if beta_schedule != "squaredcos_cap_v2":
        raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""					)

      _UpperCAmelCase						 =      betas_for_alpha_bar(__lowerCAmelCase					)

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

      # standard deviation of the initial noise distribution
      _UpperCAmelCase						 =      1.0

      # setable values
      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      torch.from_numpy(np.arange(0   ,				__lowerCAmelCase					)[::-1].copy()					)

      _UpperCAmelCase						 =      variance_type


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  torch.FloatTensor   ,				__lowerCAmelCase	:  Optional[int] = None					):
      return sample


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Union[str, torch.device] = None					):
      _UpperCAmelCase						 =      num_inference_steps
      _UpperCAmelCase						 =      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      _UpperCAmelCase						 =      (np.arange(0   ,				__lowerCAmelCase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
      _UpperCAmelCase						 =      torch.from_numpy(__lowerCAmelCase					).to(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				__lowerCAmelCase	:  List[str]=None   ,				__lowerCAmelCase	:  str=None					):
      if prev_timestep is None:
        _UpperCAmelCase						 =      t - 1

      _UpperCAmelCase						 =      self.alphas_cumprod[t]
      _UpperCAmelCase						 =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      _UpperCAmelCase						 =      1 - alpha_prod_t
      _UpperCAmelCase						 =      1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        _UpperCAmelCase						 =      self.betas[t]
      else:
        _UpperCAmelCase						 =      1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      _UpperCAmelCase						 =      beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
        _UpperCAmelCase						 =      self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
        _UpperCAmelCase						 =      torch.log(torch.clamp(__lowerCAmelCase   ,				min=1e-2_0					)					)
        _UpperCAmelCase						 =      torch.exp(0.5 * variance					)
      elif variance_type == "learned_range":
        # NOTE difference with DDPM scheduler
        _UpperCAmelCase						 =      variance.log()
        _UpperCAmelCase						 =      beta.log()

        _UpperCAmelCase						 =      (predicted_variance + 1) / 2
        _UpperCAmelCase						 =      frac * max_log + (1 - frac) * min_log

      return variance


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  torch.FloatTensor   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  torch.FloatTensor   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  bool = True   ,				):
      _UpperCAmelCase						 =      timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
        _UpperCAmelCase ,				_UpperCAmelCase						 =      torch.split(__lowerCAmelCase   ,				sample.shape[1]   ,				dim=1					)
      else:
        _UpperCAmelCase						 =      None

      # 1. compute alphas, betas
      if prev_timestep is None:
        _UpperCAmelCase						 =      t - 1

      _UpperCAmelCase						 =      self.alphas_cumprod[t]
      _UpperCAmelCase						 =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      _UpperCAmelCase						 =      1 - alpha_prod_t
      _UpperCAmelCase						 =      1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        _UpperCAmelCase						 =      self.betas[t]
        _UpperCAmelCase						 =      self.alphas[t]
      else:
        _UpperCAmelCase						 =      1 - alpha_prod_t / alpha_prod_t_prev
        _UpperCAmelCase						 =      1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
        _UpperCAmelCase						 =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
        _UpperCAmelCase						 =      model_output
      else:
        raise ValueError(
            f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
            """ for the UnCLIPScheduler."""					)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
        _UpperCAmelCase						 =      torch.clamp(
            __lowerCAmelCase   ,				-self.config.clip_sample_range   ,				self.config.clip_sample_range					)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _UpperCAmelCase						 =      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      _UpperCAmelCase						 =      alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _UpperCAmelCase						 =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      _UpperCAmelCase						 =      0
      if t > 0:
        _UpperCAmelCase						 =      randn_tensor(
            model_output.shape   ,				dtype=model_output.dtype   ,				generator=__lowerCAmelCase   ,				device=model_output.device					)

        _UpperCAmelCase						 =      self._get_variance(
            __lowerCAmelCase   ,				predicted_variance=__lowerCAmelCase   ,				prev_timestep=__lowerCAmelCase   ,				)

        if self.variance_type == "fixed_small_log":
          _UpperCAmelCase						 =      variance
        elif self.variance_type == "learned_range":
          _UpperCAmelCase						 =      (0.5 * variance).exp()
        else:
          raise ValueError(
              f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
              """ for the UnCLIPScheduler."""					)

        _UpperCAmelCase						 =      variance * variance_noise

      _UpperCAmelCase						 =      pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase   ,				pred_original_sample=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  torch.FloatTensor   ,				__lowerCAmelCase	:  torch.FloatTensor   ,				__lowerCAmelCase	:  torch.IntTensor   ,				):
      # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
      _UpperCAmelCase						 =      self.alphas_cumprod.to(device=original_samples.device   ,				dtype=original_samples.dtype					)
      _UpperCAmelCase						 =      timesteps.to(original_samples.device					)

      _UpperCAmelCase						 =      alphas_cumprod[timesteps] ** 0.5
      _UpperCAmelCase						 =      sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
        _UpperCAmelCase						 =      sqrt_alpha_prod.unsqueeze(-1					)

      _UpperCAmelCase						 =      (1 - alphas_cumprod[timesteps]) ** 0.5
      _UpperCAmelCase						 =      sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
        _UpperCAmelCase						 =      sqrt_one_minus_alpha_prod.unsqueeze(-1					)

      _UpperCAmelCase						 =      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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