
'''simple docstring'''

__snake_case:  Optional[int]								=			[
    9_99,
    8_00,
    7_99,
    6_00,
    5_99,
    5_00,
    4_00,
    3_99,
    3_77,
    3_55,
    3_33,
    3_11,
    2_88,
    2_66,
    2_44,
    2_22,
    2_00,
    1_99,
    1_77,
    1_55,
    1_33,
    1_11,
    88,
    66,
    44,
    22,
    0,
]

__snake_case:  Any								=			[
    9_99,
    9_76,
    9_52,
    9_28,
    9_05,
    8_82,
    8_58,
    8_57,
    8_10,
    7_62,
    7_15,
    7_14,
    5_72,
    4_29,
    4_28,
    2_86,
    2_85,
    2_38,
    1_90,
    1_43,
    1_42,
    1_18,
    95,
    71,
    47,
    24,
    0,
]

__snake_case:  Dict								=			[
    9_99,
    9_88,
    9_77,
    9_66,
    9_55,
    9_44,
    9_33,
    9_22,
    9_11,
    9_00,
    8_99,
    8_79,
    8_59,
    8_40,
    8_20,
    8_00,
    7_99,
    7_66,
    7_33,
    7_00,
    6_99,
    6_50,
    6_00,
    5_99,
    5_00,
    4_99,
    4_00,
    3_99,
    3_50,
    3_00,
    2_99,
    2_66,
    2_33,
    2_00,
    1_99,
    1_79,
    1_59,
    1_40,
    1_20,
    1_00,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

__snake_case:  Union[str, Any]								=			[
    9_99,
    9_95,
    9_92,
    9_89,
    9_85,
    9_81,
    9_78,
    9_75,
    9_71,
    9_67,
    9_64,
    9_61,
    9_57,
    9_56,
    9_51,
    9_47,
    9_42,
    9_37,
    9_33,
    9_28,
    9_23,
    9_19,
    9_14,
    9_13,
    9_08,
    9_03,
    8_97,
    8_92,
    8_87,
    8_81,
    8_76,
    8_71,
    8_70,
    8_64,
    8_58,
    8_52,
    8_46,
    8_40,
    8_34,
    8_28,
    8_27,
    8_20,
    8_13,
    8_06,
    7_99,
    7_92,
    7_85,
    7_84,
    7_77,
    7_70,
    7_63,
    7_56,
    7_49,
    7_42,
    7_41,
    7_33,
    7_24,
    7_16,
    7_07,
    6_99,
    6_98,
    6_88,
    6_77,
    6_66,
    6_56,
    6_55,
    6_45,
    6_34,
    6_23,
    6_13,
    6_12,
    5_98,
    5_84,
    5_70,
    5_69,
    5_55,
    5_41,
    5_27,
    5_26,
    5_05,
    4_84,
    4_83,
    4_62,
    4_40,
    4_39,
    3_96,
    3_95,
    3_52,
    3_51,
    3_08,
    3_07,
    2_64,
    2_63,
    2_20,
    2_19,
    1_76,
    1_32,
    88,
    44,
    0,
]

__snake_case:  Any								=			[
    9_99,
    9_97,
    9_95,
    9_92,
    9_90,
    9_88,
    9_86,
    9_84,
    9_81,
    9_79,
    9_77,
    9_75,
    9_72,
    9_70,
    9_68,
    9_66,
    9_64,
    9_61,
    9_59,
    9_57,
    9_56,
    9_54,
    9_51,
    9_49,
    9_46,
    9_44,
    9_41,
    9_39,
    9_36,
    9_34,
    9_31,
    9_29,
    9_26,
    9_24,
    9_21,
    9_19,
    9_16,
    9_14,
    9_13,
    9_10,
    9_07,
    9_05,
    9_02,
    8_99,
    8_96,
    8_93,
    8_91,
    8_88,
    8_85,
    8_82,
    8_79,
    8_77,
    8_74,
    8_71,
    8_70,
    8_67,
    8_64,
    8_61,
    8_58,
    8_55,
    8_52,
    8_49,
    8_46,
    8_43,
    8_40,
    8_37,
    8_34,
    8_31,
    8_28,
    8_27,
    8_24,
    8_21,
    8_17,
    8_14,
    8_11,
    8_08,
    8_04,
    8_01,
    7_98,
    7_95,
    7_91,
    7_88,
    7_85,
    7_84,
    7_80,
    7_77,
    7_74,
    7_70,
    7_66,
    7_63,
    7_60,
    7_56,
    7_52,
    7_49,
    7_46,
    7_42,
    7_41,
    7_37,
    7_33,
    7_30,
    7_26,
    7_22,
    7_18,
    7_14,
    7_10,
    7_07,
    7_03,
    6_99,
    6_98,
    6_94,
    6_90,
    6_85,
    6_81,
    6_77,
    6_73,
    6_69,
    6_64,
    6_60,
    6_56,
    6_55,
    6_50,
    6_46,
    6_41,
    6_36,
    6_32,
    6_27,
    6_22,
    6_18,
    6_13,
    6_12,
    6_07,
    6_02,
    5_96,
    5_91,
    5_86,
    5_80,
    5_75,
    5_70,
    5_69,
    5_63,
    5_57,
    5_51,
    5_45,
    5_39,
    5_33,
    5_27,
    5_26,
    5_19,
    5_12,
    5_05,
    4_98,
    4_91,
    4_84,
    4_83,
    4_74,
    4_66,
    4_57,
    4_49,
    4_40,
    4_39,
    4_28,
    4_18,
    4_07,
    3_96,
    3_95,
    3_81,
    3_66,
    3_52,
    3_51,
    3_30,
    3_08,
    3_07,
    2_86,
    2_64,
    2_63,
    2_42,
    2_20,
    2_19,
    1_76,
    1_75,
    1_32,
    1_31,
    88,
    44,
    0,
]

__snake_case:  Union[str, Any]								=			[
    9_99,
    9_91,
    9_82,
    9_74,
    9_66,
    9_58,
    9_50,
    9_41,
    9_33,
    9_25,
    9_16,
    9_08,
    9_00,
    8_99,
    8_74,
    8_50,
    8_25,
    8_00,
    7_99,
    7_00,
    6_00,
    5_00,
    4_00,
    3_00,
    2_00,
    1_00,
    0,
]

__snake_case:  str								=			[
    9_99,
    9_92,
    9_85,
    9_78,
    9_71,
    9_64,
    9_57,
    9_49,
    9_42,
    9_35,
    9_28,
    9_21,
    9_14,
    9_07,
    9_00,
    8_99,
    8_79,
    8_59,
    8_40,
    8_20,
    8_00,
    7_99,
    7_66,
    7_33,
    7_00,
    6_99,
    6_50,
    6_00,
    5_99,
    5_00,
    4_99,
    4_00,
    3_99,
    3_00,
    2_99,
    2_00,
    1_99,
    1_00,
    99,
    0,
]

__snake_case:  Optional[Any]								=			[
    9_99,
    9_96,
    9_92,
    9_89,
    9_85,
    9_82,
    9_79,
    9_75,
    9_72,
    9_68,
    9_65,
    9_61,
    9_58,
    9_55,
    9_51,
    9_48,
    9_44,
    9_41,
    9_38,
    9_34,
    9_31,
    9_27,
    9_24,
    9_20,
    9_17,
    9_14,
    9_10,
    9_07,
    9_03,
    9_00,
    8_99,
    8_91,
    8_84,
    8_76,
    8_69,
    8_61,
    8_53,
    8_46,
    8_38,
    8_30,
    8_23,
    8_15,
    8_08,
    8_00,
    7_99,
    7_88,
    7_77,
    7_66,
    7_55,
    7_44,
    7_33,
    7_22,
    7_11,
    7_00,
    6_99,
    6_88,
    6_77,
    6_66,
    6_55,
    6_44,
    6_33,
    6_22,
    6_11,
    6_00,
    5_99,
    5_85,
    5_71,
    5_57,
    5_42,
    5_28,
    5_14,
    5_00,
    4_99,
    4_85,
    4_71,
    4_57,
    4_42,
    4_28,
    4_14,
    4_00,
    3_99,
    3_79,
    3_59,
    3_40,
    3_20,
    3_00,
    2_99,
    2_79,
    2_59,
    2_40,
    2_20,
    2_00,
    1_99,
    1_66,
    1_33,
    1_00,
    99,
    66,
    33,
    0,
]




'''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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					None







def 	_snake_case						(      A_       :    int     ,		A_       :    List[str]=0.999     ,		A_       :    List[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_       :    List[str]   ):
      return math.exp(t * -12.0   )

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

  a_      :	int      =							[]
  for i in range(A_   ):
    a_      :	Optional[Any]      =							i / num_diffusion_timesteps
    a_      :	List[Any]      =							(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  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = "fixed_small_log"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = "squaredcos_cap_v2"							,		):

       '''simple docstring'''

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

       a_      :	Optional[Any]      =							betas_for_alpha_bar(lowerCAmelCase_ )

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

       # standard deviation of the initial noise distribution
       a_      :	Any      =							1.0

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

       a_      :	Any      =							variance_type


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       return sample


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if prev_timestep is None:
         a_      :	Tuple      =							t - 1

       a_      :	Optional[Any]      =							self.alphas_cumprod[t]
       a_      :	Union[str, Any]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Optional[Any]      =							1 - alpha_prod_t
       a_      :	str      =							1 - alpha_prod_t_prev

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

       if variance_type is None:
         a_      :	int      =							self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
         a_      :	List[Any]      =							torch.log(torch.clamp(lowerCAmelCase_							,		min=1E-2_0 ) )
         a_      :	List[Any]      =							torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         a_      :	Tuple      =							variance.log()
         a_      :	int      =							beta.log()

         a_      :	List[Any]      =							(predicted_variance + 1) / 2
         a_      :	Dict      =							frac * max_log + (1 - frac) * min_log

       return variance


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	Tuple      =							timestep

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

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

       a_      :	int      =							self.alphas_cumprod[t]
       a_      :	List[str]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Any      =							1 - alpha_prod_t
       a_      :	Any      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	Optional[int]      =							self.betas[t]
         a_      :	Any      =							self.alphas[t]
       else:
         a_      :	List[Any]      =							1 - alpha_prod_t / alpha_prod_t_prev
         a_      :	List[str]      =							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_      :	Optional[int]      =							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_      :	Any      =							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
       a_      :	str      =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       a_      :	Dict      =							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_      :	Any      =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       a_      :	List[Any]      =							0
       if t > 0:
         a_      :	List[Any]      =							randn_tensor(
             model_output.shape							,		dtype=model_output.dtype							,		generator=lowerCAmelCase_							,		device=model_output.device )

         a_      :	List[Any]      =							self._get_variance(
             lowerCAmelCase_							,		predicted_variance=lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_							,		)

         if self.variance_type == "fixed_small_log":
           a_      :	Dict      =							variance
         elif self.variance_type == "learned_range":
           a_      :	Any      =							(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_      :	List[str]      =							variance * variance_noise

       a_      :	Optional[Any]      =							pred_prev_sample + variance

       if not return_dict:
         return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase_							,		pred_original_sample=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[str]      =							self.alphas_cumprod.to(device=original_samples.device							,		dtype=original_samples.dtype )
       a_      :	Union[str, Any]      =							timesteps.to(original_samples.device )

       a_      :	List[Any]      =							alphas_cumprod[timesteps] ** 0.5
       a_      :	Optional[Any]      =							sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
         a_      :	int      =							sqrt_alpha_prod.unsqueeze(-1 )

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

       a_      :	int      =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples


