


'''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     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






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






def _SCREAMING_SNAKE_CASE (A    ,					A=0.999    ,					A="cosine"    ,					)			->			Optional[int]:






       """simple docstring"""







       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowercase__      =					[]
       for i in range(A			):
              lowercase__      =					i / num_diffusion_timesteps
              lowercase__      =					(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     __lowerCAmelCase		(lowercase_    ,	lowercase_	):





      '''simple docstring'''






      @register_to_config
      def __init__(self       :       Union[str, Any]  ,		UpperCamelCase       :       int = 1000  ,		UpperCamelCase       :       str = "fixed_small_log"  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Optional[float] = 1.0  ,		UpperCamelCase       :       str = "epsilon"  ,		UpperCamelCase       :       str = "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(UpperCamelCase					)

             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  ,		UpperCamelCase					)[::-1].copy()					)

             lowercase__      =					variance_type



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       torch.FloatTensor  ,		UpperCamelCase       :       Optional[int] = None					):

             '''simple docstring'''







             return sample



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Union[str, torch.device] = None					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Dict=None  ,		UpperCamelCase       :       int=None  ,		UpperCamelCase       :       Dict=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(UpperCamelCase  ,		min=1E-20					)					)
                    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       :       Union[str, Any]  ,		UpperCamelCase       :       torch.FloatTensor  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       torch.FloatTensor  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Any]=None  ,		UpperCamelCase       :       bool = True  ,		):

             '''simple docstring'''







             lowercase__      =					timestep

             if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                    lowercase__     ,lowercase__      =					torch.split(UpperCamelCase  ,		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(
                        UpperCamelCase  ,		-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=UpperCamelCase  ,		device=model_output.device					)

                    lowercase__      =					self._get_variance(
                        UpperCamelCase  ,		predicted_variance=UpperCamelCase  ,		prev_timestep=UpperCamelCase  ,		)

                    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=UpperCamelCase  ,		pred_original_sample=UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       torch.FloatTensor  ,		UpperCamelCase       :       torch.FloatTensor  ,		UpperCamelCase       :       torch.IntTensor  ,		):

             '''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'''

from __future__ import annotations

from typing import Any
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       int = 6					):

             '''simple docstring'''







             lowercase__      =					None
             lowercase__      =					None
             self.create_linked_list(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					Node()
             lowercase__      =					current_node
             lowercase__      =					current_node
             lowercase__      =					current_node
             for _ in range(1  ,		UpperCamelCase					):
                    lowercase__      =					Node()
                    lowercase__      =					current_node
                    lowercase__      =					previous_node
                    lowercase__      =					current_node
             lowercase__      =					self.front
             lowercase__      =					previous_node



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return (
                 self.front == self.rear
                 and self.front is not None
                 and self.front.data is None
             )



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             self.check_can_perform_operation()
             return self.front.data if self.front else None



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       Any					):

             '''simple docstring'''







             if self.rear is None:
                    return

             self.check_is_full()
             if not self.is_empty():
                    lowercase__      =					self.rear.next
             if self.rear:
                    lowercase__      =					data



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             self.check_can_perform_operation()
             if self.rear is None or self.front is None:
                    return None
             if self.front == self.rear:
                    lowercase__      =					self.front.data
                    lowercase__      =					None
                    return data

             lowercase__      =					self.front
             lowercase__      =					old_front.next
             lowercase__      =					old_front.data
             lowercase__      =					None
             return data



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             if self.is_empty():
                    raise Exception('''Empty Queue'''					)






      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             if self.rear and self.rear.next == self.front:
                    raise Exception('''Full Queue'''					)






class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					None
             lowercase__      =					None
             lowercase__      =					None


if __name__ == "__main__":
 import doctest

 doctest.testmod()




