



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__	(       _A):



 """simple docstring"""

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:List[Any]=0.999	,   _snake_case	:Optional[int]="cosine"	,   )  ->     Dict:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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


class 	lowerCamelCase__	(       _A							,  _A):



 """simple docstring"""

 @register_to_config
 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					int = 10_00			,						__lowerCAmelCase					:					str = "fixed_small_log"			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Optional[float] = 1.0			,						__lowerCAmelCase					:					str = "epsilon"			,						__lowerCAmelCase					:					str = "squaredcos_cap_v2"			,						)   ->	Optional[int]:
    if beta_schedule != "squaredcos_cap_v2":
       raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''      )

    _A      =       betas_for_alpha_bar(__lowerCAmelCase      )

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

    # standard deviation of the initial noise distribution
    _A      =       1.0

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

    _A      =       variance_type

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					Optional[int] = None      )   ->	torch.FloatTensor:
    return sample

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Union[str, torch.device] = None      )   ->	Optional[Any]:
    _A      =       num_inference_steps
    _A      =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
    _A      =       (np.arange(0			,						__lowerCAmelCase      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
    _A      =       torch.from_numpy(__lowerCAmelCase      ).to(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Tuple=None			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					Tuple=None      )   ->	List[str]:
    if prev_timestep is None:
       _A      =       t - 1

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

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

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

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

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

    return variance

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[Any]=None			,						__lowerCAmelCase					:					bool = True			,						)   ->	Union[UnCLIPSchedulerOutput, Tuple]:
    _A      =       timestep

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

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

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

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

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

       _A      =       self._get_variance(
           __lowerCAmelCase			,						predicted_variance=__lowerCAmelCase			,						prev_timestep=__lowerCAmelCase			,						)

       if self.variance_type == "fixed_small_log":
          _A      =       variance
       elif self.variance_type == "learned_range":
          _A      =       (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      =       variance * variance_noise

    _A      =       pred_prev_sample + variance

    if not return_dict:
       return (pred_prev_sample,)

    return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase			,						pred_original_sample=__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					torch.FloatTensor			,						__lowerCAmelCase					:					torch.IntTensor			,						)   ->	torch.FloatTensor:
    # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
    _A      =       self.alphas_cumprod.to(device=original_samples.device			,						dtype=original_samples.dtype      )
    _A      =       timesteps.to(original_samples.device      )

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

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

    _A      =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples








from __future__ import annotations

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:str				)  ->     set[str]:
   _A  ,    _A      =       set(_snake_case				), [start]

   while stack:
      _A      =       stack.pop()
      explored.add(_snake_case				)
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]				):
         if adj not in explored:
            stack.append(_snake_case				)
   return explored


UpperCAmelCase_		 =					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, """A"""))



