




'''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     _a						(			lowercase__				):
       __a       :  List[Any]            =      42
       __a       :  Any            =      None






def 		snake_case_							(_a			:							List[Any]					,     _a			:							str=0.999					,     _a			:							List[str]="cosine"					,     ):


    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_a			:							Optional[Any]  ):
            return math.exp(t * -1_2.0  )

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

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







class     _a						(			lowercase__							,							lowercase__				):
       @register_to_config
       def __init__(			self		:			int     ,       lowercase		:			int = 1_000     ,       lowercase		:			str = "fixed_small_log"     ,       lowercase		:			bool = True     ,       lowercase		:			Optional[float] = 1.0     ,       lowercase		:			str = "epsilon"     ,       lowercase		:			str = "squaredcos_cap_v2"     ,       ):



           '''simple docstring'''

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

           UpperCAmelCase     =	betas_for_alpha_bar(lowercase       )

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

           # standard deviation of the initial noise distribution
           UpperCAmelCase     =	1.0

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

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



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase     =	num_inference_steps
           UpperCAmelCase     =	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           UpperCAmelCase     =	(np.arange(0     ,       lowercase       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
           UpperCAmelCase     =	torch.from_numpy(lowercase       ).to(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str     ,       lowercase		:			Optional[Any]=None     ,       lowercase		:			Any=None     ,       lowercase		:			Optional[Any]=None       ):



           '''simple docstring'''

           if prev_timestep is None:
               UpperCAmelCase     =	t - 1

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

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

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

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
               UpperCAmelCase     =	torch.log(torch.clamp(lowercase     ,       min=1E-20       )       )
               UpperCAmelCase     =	torch.exp(0.5 * variance       )
           elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               UpperCAmelCase     =	variance.log()
               UpperCAmelCase     =	beta.log()

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

           return variance
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			torch.FloatTensor     ,       lowercase		:			int     ,       lowercase		:			torch.FloatTensor     ,       lowercase		:			Optional[int] = None     ,       lowercase		:			Union[str, Any]=None     ,       lowercase		:			bool = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	timestep

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

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

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

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

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

               UpperCAmelCase     =	self._get_variance(
                   lowercase     ,       predicted_variance=lowercase     ,       prev_timestep=lowercase     ,       )

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

               UpperCAmelCase     =	variance * variance_noise

           UpperCAmelCase     =	pred_prev_sample + variance

           if not return_dict:
               return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=lowercase     ,       pred_original_sample=lowercase       )







       def 					A   (			self		:			List[Any]     ,       lowercase		:			torch.FloatTensor     ,       lowercase		:			torch.FloatTensor     ,       lowercase		:			torch.IntTensor     ,       ):



           '''simple docstring'''

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

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

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

           UpperCAmelCase     =	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples










'''simple docstring'''







from typing import Any


class     _a						:
       def __init__(			self		:			int     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	data
           UpperCAmelCase     =	None






class     _a						:
       def __init__(			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	None
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.head
           while temp is not None:
               print(temp.data     ,       end=''' '''       )
               UpperCAmelCase     =	temp.next
           print()
       def 					A   (			self		:			List[str]     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	Node(lowercase       )
           UpperCAmelCase     =	self.head
           UpperCAmelCase     =	new_node







       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			str       ):



           '''simple docstring'''

           if node_data_a == node_data_a:
               return
           else:
               UpperCAmelCase     =	self.head
               while node_a is not None and node_a.data != node_data_a:
                   UpperCAmelCase     =	node_a.next

               UpperCAmelCase     =	self.head
               while node_a is not None and node_a.data != node_data_a:
                   UpperCAmelCase     =	node_a.next

               if node_a is None or node_a is None:
                   return

               UpperCAmelCase ,  UpperCAmelCase     =	node_a.data, node_a.data


if __name__ == "__main__":
   A									=LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('After swapping')
   ll.print_list()




