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 		_lowercase						(					snake_case_       ):
 lowercase				     =   4_2
 lowercase				     =   None





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     List[Any]=0.9_9_9  ,  lowerCamelCase					:     Optional[int]="cosine"  ,  ):





 if alpha_transform_type == "cosine":

  def alpha_bar_fn(lowerCamelCase					:     List[Any]	):
   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(lowerCamelCase					:     int	):
   return math.exp(t * -1_2.0	)

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

 UpperCamelCase_					:						int =					[]
 for i in range(lowerCamelCase	):
  UpperCamelCase_					:						Optional[Any] =					i / num_diffusion_timesteps
  UpperCamelCase_					:						Tuple =					(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 		_lowercase						(					snake_case_						,      snake_case_       ):

 @register_to_config
 def __init__( self	:							int   ,      snake_case	:							str = 1_0_0_0   ,      snake_case	:							Union[str, Any] = "fixed_small_log"   ,      snake_case	:							Any = True   ,      snake_case	:							Optional[Any] = 1.0   ,      snake_case	:							int = "epsilon"   ,      snake_case	:							List[Any] = "squaredcos_cap_v2"   ,      )   ->						List[Any]:

  """simple docstring"""





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

  UpperCamelCase_					:						Any =					betas_for_alpha_bar(lowerCamelCase_			)

  UpperCamelCase_					:						Any =					1.0 - self.betas
  UpperCamelCase_					:						Tuple =					torch.cumprod(self.alphas   ,      dim=0			)
  UpperCamelCase_					:						Dict =					torch.tensor(1.0			)

  # standard deviation of the initial noise distribution
  UpperCamelCase_					:						List[str] =					1.0

  # setable values
  UpperCamelCase_					:						Dict =					None
  UpperCamelCase_					:						Any =					torch.from_numpy(np.arange(0   ,      lowerCamelCase_			)[::-1].copy()			)

  UpperCamelCase_					:						List[str] =					variance_type

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Any   ,      snake_case	:							Union[str, Any] = None			)   ->						Union[str, Any]:

  """simple docstring"""





  return sample

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							List[Any]   ,      snake_case	:							Tuple = None			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						str =					num_inference_steps
  UpperCamelCase_					:						Union[str, Any] =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
  UpperCamelCase_					:						int =					(np.arange(0   ,      lowerCamelCase_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
  UpperCamelCase_					:						Tuple =					torch.from_numpy(lowerCamelCase_			).to(lowerCamelCase_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							str   ,      snake_case	:							Dict=None   ,      snake_case	:							List[Any]=None   ,      snake_case	:							str=None			)   ->						Dict:

  """simple docstring"""





  if prev_timestep is None:
   UpperCamelCase_					:						str =					t - 1

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

  if prev_timestep == t - 1:
   UpperCamelCase_					:						List[Any] =					self.betas[t]
  else:
   UpperCamelCase_					:						Optional[Any] =					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_					:						Dict =					beta_prod_t_prev / beta_prod_t * beta

  if variance_type is None:
   UpperCamelCase_					:						List[Any] =					self.config.variance_type

  # hacks - were probably added for training stability
  if variance_type == "fixed_small_log":
   UpperCamelCase_					:						Dict =					torch.log(torch.clamp(lowerCamelCase_   ,      min=1e-20			)			)
   UpperCamelCase_					:						List[Any] =					torch.exp(0.5 * variance			)
  elif variance_type == "learned_range":
   # NOTE difference with DDPM scheduler
   UpperCamelCase_					:						Union[str, Any] =					variance.log()
   UpperCamelCase_					:						int =					beta.log()

   UpperCamelCase_					:						Optional[int] =					(predicted_variance + 1) / 2
   UpperCamelCase_					:						Tuple =					frac * max_log + (1 - frac) * min_log

  return variance

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

  """simple docstring"""





  UpperCamelCase_					:						int =					timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
   UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					torch.split(lowerCamelCase_   ,      sample.shape[1]   ,      dim=1			)
  else:
   UpperCamelCase_					:						List[str] =					None

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

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

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

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

   UpperCamelCase_					:						List[str] =					self._get_variance(
       lowerCamelCase_   ,      predicted_variance=lowerCamelCase_   ,      prev_timestep=lowerCamelCase_   ,      )

   if self.variance_type == "fixed_small_log":
    UpperCamelCase_					:						int =					variance
   elif self.variance_type == "learned_range":
    UpperCamelCase_					:						Tuple =					(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_					:						str =					variance * variance_noise

  UpperCamelCase_					:						int =					pred_prev_sample + variance

  if not return_dict:
   return (pred_prev_sample,)

  return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_   ,      pred_original_sample=lowerCamelCase_			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Dict   ,      snake_case	:							List[Any]   ,      )   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.alphas_cumprod.to(device=original_samples.device   ,      dtype=original_samples.dtype			)
  UpperCamelCase_					:						Optional[int] =					timesteps.to(original_samples.device			)

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

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

  UpperCamelCase_					:						List[str] =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples








def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     sequence[1:]       )       ):
															if rod_upper > rod_lower:
																				sequence[i] -= rod_upper - rod_lower
																				sequence[i + 1] += rod_upper - rod_lower
					return sequence


if __name__ == "__main__":
				assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
