





from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__lowerCamelCase       :     str   =     0

__lowerCamelCase       :     Tuple   =     [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__lowerCamelCase       :     Union[str, Any]   =     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__lowerCamelCase       :     Optional[Any]   =     tuple[int, int]



class 					A__   :





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   pos_x
       UpperCamelCase	:					int       	=   pos_y
       UpperCamelCase	:					Union[str, Any]       	=   (pos_y, pos_x)
       UpperCamelCase	:					Optional[int]       	=   goal_x
       UpperCamelCase	:					str       	=   goal_y
       UpperCamelCase	:					Dict       	=   g_cost
       UpperCamelCase	:					Tuple       	=   parent
       UpperCamelCase	:					Optional[int]       	=   self.calculate_heuristic()
       UpperCamelCase	:					List[str]       	=   self.g_cost + self.h_cost





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.pos_x - self.goal_x
       UpperCamelCase	:					List[str]       	=   self.pos_y - self.goal_y
       if HEURISTIC == 1:
        return abs(A_   ) + abs(A_   )
       else:
        return sqrt(dy**2 + dx**2   )





      def __lt__( self       ,							A_   ):
       '''simple docstring'''




       return self.f_cost < other.f_cost



class 					A__   :





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   Node(start[1]       ,							start[0]       ,							goal[1]       ,							goal[0]       ,							0       ,							A_   )
       UpperCamelCase	:					Tuple       	=   Node(goal[1]       ,							goal[0]       ,							goal[1]       ,							goal[0]       ,							9_9999       ,							A_   )

       UpperCamelCase	:					int       	=   [self.start]
       UpperCamelCase	:					list[Node]       	=   []

       UpperCamelCase	:					Optional[int]       	=   False





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        UpperCamelCase	:					str       	=   self.open_nodes.pop(0   )

        if current_node.pos == self.target.pos:
         return self.retrace_path(A_   )

        self.closed_nodes.append(A_   )
        UpperCamelCase	:					Tuple       	=   self.get_successors(A_   )

        for child_node in successors:
         if child_node in self.closed_nodes:
          continue

         if child_node not in self.open_nodes:
          self.open_nodes.append(A_   )
         else:
          # retrieve the best current path
          UpperCamelCase	:					Optional[Any]       	=   self.open_nodes.pop(self.open_nodes.index(A_   )   )

          if child_node.g_cost < better_node.g_cost:
           self.open_nodes.append(A_   )
          else:
           self.open_nodes.append(A_   )

       return [self.start.pos]





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   []
       for action in delta:
        UpperCamelCase	:					List[Any]       	=   parent.pos_x + action[1]
        UpperCamelCase	:					List[str]       	=   parent.pos_y + action[0]
        if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(A_   ) - 1):
         continue

        if grid[pos_y][pos_x] != 0:
         continue

        successors.append(
            Node(
                A_       ,							A_       ,							self.target.pos_y       ,							self.target.pos_x       ,							parent.g_cost + 1       ,							A_       ,							)   )
       return successors





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   node
       UpperCamelCase	:					Dict       	=   []
       while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)   )
        UpperCamelCase	:					int       	=   current_node.parent
       path.reverse()
       return path



class 					A__   :





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   AStar(A_       ,							A_   )
       UpperCamelCase	:					Any       	=   AStar(A_       ,							A_   )
       UpperCamelCase	:					Optional[int]       	=   False





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
        self.fwd_astar.open_nodes.sort()
        self.bwd_astar.open_nodes.sort()
        UpperCamelCase	:					List[Any]       	=   self.fwd_astar.open_nodes.pop(0   )
        UpperCamelCase	:					List[str]       	=   self.bwd_astar.open_nodes.pop(0   )

        if current_bwd_node.pos == current_fwd_node.pos:
         return self.retrace_bidirectional_path(
             A_       ,							A_   )

        self.fwd_astar.closed_nodes.append(A_   )
        self.bwd_astar.closed_nodes.append(A_   )

        UpperCamelCase	:					Union[str, Any]       	=   current_bwd_node
        UpperCamelCase	:					Union[str, Any]       	=   current_fwd_node

        UpperCamelCase	:					Dict       	=   {
            self.fwd_astar: self.fwd_astar.get_successors(A_   ),
            self.bwd_astar: self.bwd_astar.get_successors(A_   ),
        }

        for astar in [self.fwd_astar, self.bwd_astar]:
         for child_node in successors[astar]:
          if child_node in astar.closed_nodes:
           continue

          if child_node not in astar.open_nodes:
           astar.open_nodes.append(A_   )
          else:
           # retrieve the best current path
           UpperCamelCase	:					Optional[Any]       	=   astar.open_nodes.pop(
               astar.open_nodes.index(A_   )   )

           if child_node.g_cost < better_node.g_cost:
            astar.open_nodes.append(A_   )
           else:
            astar.open_nodes.append(A_   )

       return [self.fwd_astar.start.pos]





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   self.fwd_astar.retrace_path(A_   )
       UpperCamelCase	:					Optional[int]       	=   self.bwd_astar.retrace_path(A_   )
       bwd_path.pop()
       bwd_path.reverse()
       UpperCamelCase	:					int       	=   fwd_path + bwd_path
       return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    __lowerCamelCase       :     Optional[Any]   =     (0, 0)
    __lowerCamelCase       :     Any   =     (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    __lowerCamelCase       :     List[Any]   =     time.time()
    __lowerCamelCase       :     str   =     AStar(init, goal)
    __lowerCamelCase       :     int   =     a_star.search()
    __lowerCamelCase       :     List[str]   =     time.time() - start_time
    print(f"""AStar execution time = {end_time:f} seconds""")

    __lowerCamelCase       :     Union[str, Any]   =     time.time()
    __lowerCamelCase       :     Any   =     BidirectionalAStar(init, goal)
    __lowerCamelCase       :     Optional[int]   =     time.time() - bd_start_time
    print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
