




'''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
class      UpperCamelCase       (	snake_case__       ):
  a     :torch.FloatTensor
  a     :torch.FloatTensor






def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=0.999   ,	UpperCAmelCase_="cosine"   ,	):



    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    _a				:							Union[str, Any]							       =      []
    for i in range(snake_case_						):
        _a				:							List[str]							       =      i / num_diffusion_timesteps
        _a				:							List[str]							       =      (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      UpperCamelCase       (	snake_case__      ,		snake_case__       ):
  a     :Any          =					1



  @register_to_config
  def __init__(			self		,     __lowerCAmelCase = 1000		,     __lowerCAmelCase = 0.0001		,     __lowerCAmelCase = 0.02		,     __lowerCAmelCase = "linear"		,     __lowerCAmelCase = 0.0		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      if trained_betas is not None:
          _a				:							Union[str, Any]							       =      torch.from_numpy(__snake_case )
      elif beta_schedule == "linear":
          _a				:							List[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.
          _a				:							int							       =      (
              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
          _a				:							Any							       =      betas_for_alpha_bar(__snake_case )
      elif beta_schedule == "sigmoid":
          # GeoDiff sigmoid schedule
          _a				:							List[str]							       =      torch.linspace(-6		,     6		,     __snake_case )
          _a				:							List[Any]							       =      torch.sigmoid(__snake_case ) * (beta_end - beta_start) + beta_start
      else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

      _a				:							Optional[Any]							       =      torch.tensor(1.0 )

      # standard deviation of the initial noise distribution
      _a				:							str							       =      1.0

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

      _a				:							Optional[Any]							       =      eta



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      return sample



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = 10		,     __lowerCAmelCase = 10		,     __lowerCAmelCase = None		,     ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      min(self.config.num_train_timesteps		,     __snake_case )
      _a				:							Optional[Any]							       =      num_inference_steps

      _a				:							Dict							       =      []

      _a				:							Optional[Any]							       =      {}
      for j in range(0		,     num_inference_steps - jump_length		,     __snake_case ):
          _a				:							int							       =      jump_n_sample - 1

      _a				:							Tuple							       =      num_inference_steps
      while t >= 1:
          _a				:							List[str]							       =      t - 1
          timesteps.append(__snake_case )

          if jumps.get(__snake_case		,     0 ) > 0:
              _a				:							Dict							       =      jumps[t] - 1
              for _ in range(__snake_case ):
                  _a				:							Optional[Any]							       =      t + 1
                  timesteps.append(__snake_case )

      _a				:							Optional[int]							       =      np.array(__snake_case ) * (self.config.num_train_timesteps // self.num_inference_steps)
      _a				:							Union[str, Any]							       =      torch.from_numpy(__snake_case ).to(__snake_case )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      t - self.config.num_train_timesteps // self.num_inference_steps

      _a				:							List[str]							       =      self.alphas_cumprod[t]
      _a				:							Tuple							       =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
      _a				:							List[Any]							       =      1 - alpha_prod_t
      _a				:							List[Any]							       =      1 - 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
      # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
      # without eta.
      # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
      _a				:							int							       =      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      timestep
      _a				:							Union[str, Any]							       =      timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 1. compute alphas, betas
      _a				:							Optional[Any]							       =      self.alphas_cumprod[t]
      _a				:							Dict							       =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
      _a				:							Any							       =      1 - alpha_prod_t

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      _a				:							str							       =      (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
          _a				:							Any							       =      torch.clamp(__snake_case		,     -1		,     1 )

      # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
      # substitute formula (7) in the algorithm coming from DDPM paper
      # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
      # DDIM schedule gives the same results as DDPM with eta = 1.0
      # Noise is being reused in 7. and 8., but no impact on quality has
      # been observed.

      # 5. Add noise
      _a				:							Union[str, Any]							       =      model_output.device
      _a				:							Optional[int]							       =      randn_tensor(model_output.shape		,     generator=__snake_case		,     device=__snake_case		,     dtype=model_output.dtype )
      _a				:							Any							       =      self.eta * self._get_variance(__snake_case ) ** 0.5

      _a				:							List[str]							       =      0
      if t > 0 and self.eta > 0:
          _a				:							List[str]							       =      std_dev_t * noise

      # 6. compute "direction pointing to x_t" of formula (12)
      # from https://arxiv.org/pdf/2010.02502.pdf
      _a				:							int							       =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _a				:							Dict							       =      alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

      # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
      _a				:							int							       =      (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

      # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
      _a				:							Tuple							       =      mask * prev_known_part + (1.0 - mask) * prev_unknown_part

      if not return_dict:
          return (
              pred_prev_sample,
              pred_original_sample,
          )

      return RePaintSchedulerOutput(prev_sample=__snake_case		,     pred_original_sample=__snake_case )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=None ):







      '''simple docstring'''


      _a				:							Any							       =      self.config.num_train_timesteps // self.num_inference_steps

      for i in range(__snake_case ):
          _a				:							Any							       =      self.betas[timestep + i]
          if sample.device.type == "mps":
              # randn does not work reproducibly on mps
              _a				:							Optional[int]							       =      randn_tensor(sample.shape		,     dtype=sample.dtype		,     generator=__snake_case )
              _a				:							int							       =      noise.to(sample.device )
          else:
              _a				:							List[Any]							       =      randn_tensor(sample.shape		,     generator=__snake_case		,     device=sample.device		,     dtype=sample.dtype )

          # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
          _a				:							Any							       =      (1 - beta) ** 0.5 * sample + beta**0.5 * noise

      return sample



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.""" )



  def __len__(			self ):







      '''simple docstring'''


      return self.config.num_train_timesteps




from typing import TYPE_CHECKING

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


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
