






"""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 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					42
    __magic_name__   					=					None




def       snake_case  (A_				:Optional[Any]     ,      A_				:Dict=0.999     ,      A_				:Union[str, Any]="cosine"     ,      ):







  '''simple docstring'''

  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  a					:      List[Any]    =       []
  for i in range(A_				):
    a					:      List[str]    =       i / num_diffusion_timesteps
    a					:      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 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase	):





    @register_to_config
    def __init__(							self     : List[Any]    ,		A     : int = 1_0_0_0    ,		A     : str = "fixed_small_log"    ,		A     : bool = True    ,		A     : Optional[float] = 1.0    ,		A     : str = "epsilon"    ,		A     : str = "squaredcos_cap_v2"    ,		):


      '''simple docstring'''




      if beta_schedule != "squaredcos_cap_v2":
        raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''      )

      a					:      Any    =       betas_for_alpha_bar(A      )

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

      # standard deviation of the initial noise distribution
      a					:      Optional[int]    =       1.0

      # setable values
      a					:      Optional[Any]    =       None
      a					:      List[Any]    =       torch.from_numpy(np.arange(0    ,		A      )[::-1].copy()      )

      a					:      Any    =       variance_type





    def     lowerCamelCase__				(							self     : str    ,		A     : torch.FloatTensor    ,		A     : Optional[int] = None      ):


      '''simple docstring'''




      return sample





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : int    ,		A     : Union[str, torch.device] = None      ):


      '''simple docstring'''




      a					:      Optional[int]    =       num_inference_steps
      a					:      List[str]    =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      a					:      Dict    =       (np.arange(0    ,		A      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
      a					:      Any    =       torch.from_numpy(A      ).to(A      )





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : Optional[Any]    ,		A     : Tuple=None    ,		A     : Tuple=None    ,		A     : str=None      ):


      '''simple docstring'''




      if prev_timestep is None:
        a					:      int    =       t - 1

      a					:      Optional[Any]    =       self.alphas_cumprod[t]
      a					:      Dict    =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      a					:      int    =       1 - alpha_prod_t
      a					:      int    =       1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        a					:      List[str]    =       self.betas[t]
      else:
        a					:      Optional[int]    =       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
      a					:      Tuple    =       beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
        a					:      List[Any]    =       self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
        a					:      List[str]    =       torch.log(torch.clamp(A    ,		min=1E-20      )      )
        a					:      Union[str, Any]    =       torch.exp(0.5 * variance      )
      elif variance_type == "learned_range":
        # NOTE difference with DDPM scheduler
        a					:      str    =       variance.log()
        a					:      int    =       beta.log()

        a					:      str    =       (predicted_variance + 1) / 2
        a					:      Any    =       frac * max_log + (1 - frac) * min_log

      return variance





    def     lowerCamelCase__				(							self     : Any    ,		A     : torch.FloatTensor    ,		A     : int    ,		A     : torch.FloatTensor    ,		A     : Optional[int] = None    ,		A     : Dict=None    ,		A     : bool = True    ,		):


      '''simple docstring'''




      a					:      List[str]    =       timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
        a,					a					:      str    =       torch.split(A    ,		sample.shape[1]    ,		dim=1      )
      else:
        a					:      Optional[int]    =       None

      # 1. compute alphas, betas
      if prev_timestep is None:
        a					:      List[str]    =       t - 1

      a					:      Tuple    =       self.alphas_cumprod[t]
      a					:      Optional[Any]    =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      a					:      Tuple    =       1 - alpha_prod_t
      a					:      List[Any]    =       1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        a					:      Union[str, Any]    =       self.betas[t]
        a					:      Optional[int]    =       self.alphas[t]
      else:
        a					:      int    =       1 - alpha_prod_t / alpha_prod_t_prev
        a					:      Tuple    =       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":
        a					:      List[str]    =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
        a					:      Tuple    =       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:
        a					:      Union[str, Any]    =       torch.clamp(
            A    ,		-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
      a					:      Union[str, Any]    =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      a					:      Optional[Any]    =       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
      a					:      str    =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      a					:      Tuple    =       0
      if t > 0:
        a					:      Optional[Any]    =       randn_tensor(
            model_output.shape    ,		dtype=model_output.dtype    ,		generator=A    ,		device=model_output.device      )

        a					:      List[str]    =       self._get_variance(
            A    ,		predicted_variance=A    ,		prev_timestep=A    ,		)

        if self.variance_type == "fixed_small_log":
          a					:      int    =       variance
        elif self.variance_type == "learned_range":
          a					:      List[str]    =       (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.'      )

        a					:      int    =       variance * variance_noise

      a					:      Optional[int]    =       pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=A    ,		pred_original_sample=A      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : torch.FloatTensor    ,		A     : torch.FloatTensor    ,		A     : torch.IntTensor    ,		):


      '''simple docstring'''




      a					:      int    =       self.alphas_cumprod.to(device=original_samples.device    ,		dtype=original_samples.dtype      )
      a					:      List[str]    =       timesteps.to(original_samples.device      )

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

      a					:      Dict    =       (1 - alphas_cumprod[timesteps]) ** 0.5
      a					:      Optional[Any]    =       sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape      ) < len(original_samples.shape      ):
        a					:      Tuple    =       sqrt_one_minus_alpha_prod.unsqueeze(-1      )

      a					:      Union[str, Any]    =       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_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




