





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				):
   _UpperCamelCase   :		torch.FloatTensor
   _UpperCamelCase   :		Optional[torch.FloatTensor]			 =	None






def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=0.9_9_9		,						_SCREAMING_SNAKE_CASE="cosine"		,						)    ->       Tuple:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_SCREAMING_SNAKE_CASE		):
      return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_SCREAMING_SNAKE_CASE		):
      return math.exp(t * -1_2.0		)

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

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


class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase				):


   @register_to_config
   def __init__(   self      :    int      ,					a      :    int = 1_000      ,					a      :    str = "fixed_small_log"      ,					a      :    bool = True      ,					a      :    Optional[float] = 1.0      ,					a      :    str = "epsilon"      ,					a      :    str = "squaredcos_cap_v2"      ,					)->       str:





     """simple docstring"""







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

     lowercase__           =  betas_for_alpha_bar(a	)

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

     lowercase__           =  variance_type


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    torch.FloatTensor      ,					a      :    Optional[int] = None	)->       torch.FloatTensor:





     """simple docstring"""







     return sample


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int      ,					a      :    Union[str, torch.device] = None	)->       Tuple:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    List[str]      ,					a      :    List[str]=None      ,					a      :    Optional[int]=None      ,					a      :    str=None	)->       Dict:





     """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(a      ,					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 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    torch.FloatTensor      ,					a      :    int      ,					a      :    torch.FloatTensor      ,					a      :    Optional[int] = None      ,					a      :    Optional[Any]=None      ,					a      :    bool = True      ,					)->       Union[UnCLIPSchedulerOutput, Tuple]:





     """simple docstring"""







     lowercase__           =  timestep

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

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

       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=a      ,					pred_original_sample=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    torch.FloatTensor      ,					a      :    torch.FloatTensor      ,					a      :    torch.IntTensor      ,					)->       torch.FloatTensor:





     """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









class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Dict	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  [
         [],
         [],
         [],
     ]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    int      ,					a      :    int	)->       None:





     """simple docstring"""







     try:
       if len(self.queues[priority]	) >= 100:
         raise OverflowError('Maximum queue size is 100'	)
       self.queues[priority].append(a	)
     except IndexError:
       raise ValueError('Valid priorities are 0, 1, and 2'	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       int:





     """simple docstring"""







     for queue in self.queues:
       if queue:
         return queue.pop(0	)
     raise UnderFlowError('All queues are empty'	)


   def __str__(   self      :    Dict	)->       str:





     """simple docstring"""







     return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues	)	)






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[Any]	)->       Any:





     """simple docstring"""







     lowercase__           =  []


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     if len(self.queue	) == 100:
       raise OverFlowError('Maximum queue size is 100'	)
     self.queue.append(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       int:





     """simple docstring"""







     if not self.queue:
       raise UnderFlowError('The queue is empty'	)
     else:
       lowercase__           =  min(self.queue	)
       self.queue.remove(a	)
       return data


   def __str__(   self      :    Union[str, Any]	)->       str:





     """simple docstring"""







     return str(self.queue	)






def       __UpperCamelCase  ()    ->       str:
  lowercase__           =  FixedPriorityQueue()
  fpq.enqueue(0		,						10		)
  fpq.enqueue(1		,						70		)
  fpq.enqueue(0		,						100		)
  fpq.enqueue(2		,						1		)
  fpq.enqueue(2		,						5		)
  fpq.enqueue(1		,						7		)
  fpq.enqueue(2		,						4		)
  fpq.enqueue(1		,						64		)
  fpq.enqueue(0		,						128		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)




def       __UpperCamelCase  ()    ->       List[str]:
  lowercase__           =  ElementPriorityQueue()
  epq.enqueue(10		)
  epq.enqueue(70		)
  epq.enqueue(100		)
  epq.enqueue(1		)
  epq.enqueue(5		)
  epq.enqueue(7		)
  epq.enqueue(4		)
  epq.enqueue(64		)
  epq.enqueue(128		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()


