


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__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     torch.FloatTensor
       lowerCamelCase__:     Optional[torch.FloatTensor]							    =     None



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=0.9_99  ,  __UpperCamelCase="cosine"  ,  ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__UpperCamelCase ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

     elif alpha_transform_type == "exp":

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

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

     a_    :     List[Any]										=    []
     for i in range(__UpperCamelCase ):
          a_    :     str										=    i / num_diffusion_timesteps
          a_    :     str										=    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )  ,  __UpperCamelCase ) )
     return torch.tensor(__UpperCamelCase  ,  dtype=torch.floataa )


class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):

       @register_to_config
       def __init__(   self      :     str      ,							lowerCamelCase_      :     int = 1_0_0_0      ,							lowerCamelCase_      :     str = "fixed_small_log"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[float] = 1.0      ,							lowerCamelCase_      :     str = "epsilon"      ,							lowerCamelCase_      :     str = "squaredcos_cap_v2"      ,							):
            if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

            a_    :     Dict										=    betas_for_alpha_bar(lowerCamelCase_    )

            a_    :     List[Any]										=    1.0 - self.betas
            a_    :     List[str]										=    torch.cumprod(self.alphas      ,							dim=0    )
            a_    :     List[Any]										=    torch.tensor(1.0    )

            # standard deviation of the initial noise distribution
            a_    :     int										=    1.0

            # setable values
            a_    :     List[str]										=    None
            a_    :     Optional[int]										=    torch.from_numpy(np.arange(0      ,							lowerCamelCase_    )[::-1].copy()    )

            a_    :     List[Any]										=    variance_type

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None    ):
            return sample

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Union[str, torch.device] = None    ):
            a_    :     Tuple										=    num_inference_steps
            a_    :     Tuple										=    (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
            a_    :     Tuple										=    (np.arange(0      ,							lowerCamelCase_    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
            a_    :     Dict										=    torch.from_numpy(lowerCamelCase_    ).to(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     str=None    ):
            if prev_timestep is None:
                 a_    :     List[Any]										=    t - 1

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

            if prev_timestep == t - 1:
                 a_    :     int										=    self.betas[t]
            else:
                 a_    :     Optional[int]										=    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_    :     int										=    beta_prod_t_prev / beta_prod_t * beta

            if variance_type is None:
                 a_    :     Optional[Any]										=    self.config.variance_type

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

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

            return variance

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     List[str]=None      ,							lowerCamelCase_      :     bool = True      ,							):
            a_    :     List[str]										=    timestep

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

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

            a_    :     Tuple										=    self.alphas_cumprod[t]
            a_    :     Any										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     int										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

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

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

                 a_    :     Tuple										=    self._get_variance(
                     lowerCamelCase_      ,							predicted_variance=lowerCamelCase_      ,							prev_timestep=lowerCamelCase_      ,							)

                 if self.variance_type == "fixed_small_log":
                      a_    :     str										=    variance
                 elif self.variance_type == "learned_range":
                      a_    :     Optional[Any]										=    (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_    :     List[str]										=    variance * variance_noise

            a_    :     List[Any]										=    pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample,)

            return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_      ,							pred_original_sample=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.IntTensor      ,							):
            # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
            a_    :     Tuple										=    self.alphas_cumprod.to(device=original_samples.device      ,							dtype=original_samples.dtype    )
            a_    :     Union[str, Any]										=    timesteps.to(original_samples.device    )

            a_    :     List[str]										=    alphas_cumprod[timesteps] ** 0.5
            a_    :     Tuple										=    sqrt_alpha_prod.flatten()
            while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
                 a_    :     Dict										=    sqrt_alpha_prod.unsqueeze(-1    )

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

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







from heapq import heappop, heappush

import numpy as np



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_			,			a_    :     Any										=    grid.shape
     a_    :     Dict										=    [-1, 1, 0, 0]
     a_    :     List[Any]										=    [0, 0, -1, 1]
     if allow_diagonal:
          dx += [-1, -1, 1, 1]
          dy += [-1, 1, -1, 1]

     a_			,			a_    :     int										=    [(0, source)], set()
     a_    :     Optional[Any]										=    np.full((rows, cols)  ,  np.inf )
     a_    :     Tuple										=    0
     a_    :     Optional[Any]										=    np.empty((rows, cols)  ,  dtype=__UpperCamelCase )
     a_    :     Optional[Any]										=    None

     while queue:
          ((a_)			,			(a_))    :     Any										=    heappop(__UpperCamelCase )
          if (x, y) in visited:
               continue
          visited.add((x, y) )

          if (x, y) == destination:
               a_    :     Tuple										=    []
               while (x, y) != source:
                    path.append((x, y) )
                    a_			,			a_    :     Any										=    predecessors[x, y]
               path.append(__UpperCamelCase )  # add the source manually
               path.reverse()
               return matrix[destination], path

          for i in range(len(__UpperCamelCase ) ):
               a_			,			a_    :     Dict										=    x + dx[i], y + dy[i]
               if 0 <= nx < rows and 0 <= ny < cols:
                    a_    :     List[Any]										=    grid[nx][ny]
                    if next_node == 1 and matrix[nx, ny] > dist + 1:
                         heappush(__UpperCamelCase  ,  (dist + 1, (nx, ny)) )
                         a_    :     List[str]										=    dist + 1
                         a_    :     Union[str, Any]										=    (x, y)

     return np.inf, []


if __name__ == "__main__":
       import doctest

       doctest.testmod()



