






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 					SCREAMING_SNAKE_CASE_  (       _UpperCAmelCase ):
       """simple docstring"""


       __lowerCAmelCase    :		torch.FloatTensor
       __lowerCAmelCase    :		Optional[torch.FloatTensor]					=None





def        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Union[str, Any]=0.999      ,    _snake_case				:       Tuple="cosine"      ,    )					->	Optional[Any]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_snake_case				:       Optional[int]):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

   elif alpha_transform_type == "exp":

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

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

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






class 					SCREAMING_SNAKE_CASE_  (       _UpperCAmelCase  ,						_UpperCAmelCase ):
       """simple docstring"""


       @register_to_config
       def __init__(				self	:Optional[Any],      snake_case	:Tuple = 1000,      snake_case	:List[str] = "fixed_small_log",      snake_case	:Any = True,      snake_case	:List[Any] = 1.0,      snake_case	:Any = "epsilon",      snake_case	:Optional[Any] = "squaredcos_cap_v2",      ):



          """simple docstring"""

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

          _lowercase              =betas_for_alpha_bar(SCREAMING_SNAKE_CASE_)

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

          # standard deviation of the initial noise distribution
          _lowercase              =1.0

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

          _lowercase              =variance_type





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Any,      snake_case	:Any = None):



          """simple docstring"""

          return sample





       def 				UpperCamelCase__						(				self	:str,      snake_case	:int,      snake_case	:Tuple = None):



          """simple docstring"""

          _lowercase              =num_inference_steps
          _lowercase              =(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          _lowercase              =(np.arange(0,      SCREAMING_SNAKE_CASE_) * step_ratio).round()[::-1].copy().astype(np.intaa)
          _lowercase              =torch.from_numpy(SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Dict,      snake_case	:str=None,      snake_case	:List[Any]=None,      snake_case	:Any=None):



          """simple docstring"""

          if prev_timestep is None:
             _lowercase              =t - 1

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

          if prev_timestep == t - 1:
             _lowercase              =self.betas[t]
          else:
             _lowercase              =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
          _lowercase              =beta_prod_t_prev / beta_prod_t * beta

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

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

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

          return variance





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:str,      snake_case	:List[Any],      snake_case	:Tuple,      snake_case	:List[Any] = None,      snake_case	:Optional[Any]=None,      snake_case	:int = True,      ):



          """simple docstring"""

          _lowercase              =timestep

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

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

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

          if prev_timestep == t - 1:
             _lowercase              =self.betas[t]
             _lowercase              =self.alphas[t]
          else:
             _lowercase              =1 - alpha_prod_t / alpha_prod_t_prev
             _lowercase              =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":
             _lowercase              =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
             _lowercase              =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:
             _lowercase              =torch.clamp(
                 SCREAMING_SNAKE_CASE_,      -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
          _lowercase              =(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          _lowercase              =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
          _lowercase              =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

             _lowercase              =self._get_variance(
                 SCREAMING_SNAKE_CASE_,      predicted_variance=SCREAMING_SNAKE_CASE_,      prev_timestep=SCREAMING_SNAKE_CASE_,      )

             if self.variance_type == "fixed_small_log":
                _lowercase              =variance
             elif self.variance_type == "learned_range":
                _lowercase              =(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.')

             _lowercase              =variance * variance_noise

          _lowercase              =pred_prev_sample + variance

          if not return_dict:
             return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_,      pred_original_sample=SCREAMING_SNAKE_CASE_)





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:str,      snake_case	:Any,      snake_case	:str,      ):



          """simple docstring"""

          _lowercase              =self.alphas_cumprod.to(device=original_samples.device,      dtype=original_samples.dtype)
          _lowercase              =timesteps.to(original_samples.device)

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

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

          _lowercase              =sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples






'''simple docstring'''



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A__						:   Any				=    re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")





@total_ordering
@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str
    lowerCamelCase    :   Optional[str]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						_str_to_version_tuple(self.version_str      )







    def __repr__(				self      )							->	Any:
         return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'







    @property
    def  lowercase_							(				self      )							->	int:
         return self.major, self.minor, self.patch







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return Version(SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return other
         raise TypeError(f'{other} (type {type(SCREAMING_SNAKE_CASE_      )}) cannot be compared to version.'      )







    def __eq__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         try:
              __lowerCamelCase  :      Union[str, Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         except (TypeError, ValueError):
              return False
         else:
              return self.tuple == other.tuple







    def __lt__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      List[Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         return self.tuple < other.tuple







    def __hash__(				self      )							->	List[str]:
         return hash(_version_tuple_to_str(self.tuple      )      )







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      str														=						{f.name for f in dataclasses.fields(cls      )}
         return cls(**{k: v for k, v in dic.items() if k in field_names}      )







    def  lowercase_							(				self      )							->	str:
         return self.version_str







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						str:
     __lowerCamelCase  :      str														=						_VERSION_REG.match(UpperCAmelCase_				)
     if not res:
          raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'				)
     return tuple(int(UpperCAmelCase_				) for v in [res.group('major'				), res.group('minor'				), res.group('patch'				)]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[str]				)	->						Dict:
     return ".".join(str(UpperCAmelCase_				) for v in version_tuple				)






