


"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  4_2







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__	):

 '''simple docstring'''

 @register_to_config
 def __init__(     self		,				_UpperCAmelCase = 6_55_36		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 2		,				_UpperCAmelCase = 2		,				_UpperCAmelCase = 0		,				_UpperCAmelCase = "fourier"		,				_UpperCAmelCase = True		,				_UpperCAmelCase = False		,				_UpperCAmelCase = 0.0		,				_UpperCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,				_UpperCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,				_UpperCAmelCase = "UNetMidBlock1D"		,				_UpperCAmelCase = None		,				_UpperCAmelCase = (32, 32, 64)		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 8		,				_UpperCAmelCase = 1		,				_UpperCAmelCase = False		,				):
       super().__init__()
       snake_case_	=				sample_size

       # time
       if time_embedding_type == "fourier":
             snake_case_	=				GaussianFourierProjection(
                 embedding_size=8		,				set_W_to_weight=_UpperCAmelCase		,				log=_UpperCAmelCase		,				flip_sin_to_cos=_UpperCAmelCase )
             snake_case_	=				2 * block_out_channels[0]
       elif time_embedding_type == "positional":
             snake_case_	=				Timesteps(
                 block_out_channels[0]		,				flip_sin_to_cos=_UpperCAmelCase		,				downscale_freq_shift=_UpperCAmelCase )
             snake_case_	=				block_out_channels[0]

       if use_timestep_embedding:
             snake_case_	=				block_out_channels[0] * 4
             snake_case_	=				TimestepEmbedding(
                 in_channels=_UpperCAmelCase		,				time_embed_dim=_UpperCAmelCase		,				act_fn=_UpperCAmelCase		,				out_dim=block_out_channels[0]		,				)

       snake_case_	=				nn.ModuleList([] )
       snake_case_	=				None
       snake_case_	=				nn.ModuleList([] )
       snake_case_	=				None

       # down
       snake_case_	=				in_channels
       for i, down_block_type in enumerate(_UpperCAmelCase ):
             snake_case_	=				output_channel
             snake_case_	=				block_out_channels[i]

             if i == 0:
                   input_channel += extra_in_channels

             snake_case_	=				i == len(_UpperCAmelCase ) - 1

             snake_case_	=				get_down_block(
                 _UpperCAmelCase		,				num_layers=_UpperCAmelCase		,				in_channels=_UpperCAmelCase		,				out_channels=_UpperCAmelCase		,				temb_channels=block_out_channels[0]		,				add_downsample=not is_final_block or downsample_each_block		,				)
             self.down_blocks.append(_UpperCAmelCase )

       # mid
       snake_case_	=				get_mid_block(
           _UpperCAmelCase		,				in_channels=block_out_channels[-1]		,				mid_channels=block_out_channels[-1]		,				out_channels=block_out_channels[-1]		,				embed_dim=block_out_channels[0]		,				num_layers=_UpperCAmelCase		,				add_downsample=_UpperCAmelCase		,				)

       # up
       snake_case_	=				list(reversed(_UpperCAmelCase ) )
       snake_case_	=				reversed_block_out_channels[0]
       if out_block_type is None:
             snake_case_	=				out_channels
       else:
             snake_case_	=				block_out_channels[0]

       for i, up_block_type in enumerate(_UpperCAmelCase ):
             snake_case_	=				output_channel
             snake_case_	=				(
                 reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase ) - 1 else final_upsample_channels
             )

             snake_case_	=				i == len(_UpperCAmelCase ) - 1

             snake_case_	=				get_up_block(
                 _UpperCAmelCase		,				num_layers=_UpperCAmelCase		,				in_channels=_UpperCAmelCase		,				out_channels=_UpperCAmelCase		,				temb_channels=block_out_channels[0]		,				add_upsample=not is_final_block		,				)
             self.up_blocks.append(_UpperCAmelCase )
             snake_case_	=				output_channel

       # out
       snake_case_	=				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,				32 )
       snake_case_	=				get_out_block(
           out_block_type=_UpperCAmelCase		,				num_groups_out=_UpperCAmelCase		,				embed_dim=block_out_channels[0]		,				out_channels=_UpperCAmelCase		,				act_fn=_UpperCAmelCase		,				fc_dim=block_out_channels[-1] // 4		,				)





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = True		,				):
       snake_case_	=				timestep
       if not torch.is_tensor(_UpperCAmelCase ):
             snake_case_	=				torch.tensor([timesteps]		,				dtype=torch.long		,				device=sample.device )
       elif torch.is_tensor(_UpperCAmelCase ) and len(timesteps.shape ) == 0:
             snake_case_	=				timesteps[None].to(sample.device )

       snake_case_	=				self.time_proj(_UpperCAmelCase )
       if self.config.use_timestep_embedding:
             snake_case_	=				self.time_mlp(_UpperCAmelCase )
       else:
             snake_case_	=				timestep_embed[..., None]
             snake_case_	=				timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
             snake_case_	=				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

       # 2. down
       snake_case_	=				()
       for downsample_block in self.down_blocks:
             snake_case_	,   snake_case_	=				downsample_block(hidden_states=_UpperCAmelCase		,				temb=_UpperCAmelCase )
             down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
             snake_case_	=				self.mid_block(_UpperCAmelCase		,				_UpperCAmelCase )

       # 4. up
       for i, upsample_block in enumerate(self.up_blocks ):
             snake_case_	=				down_block_res_samples[-1:]
             snake_case_	=				down_block_res_samples[:-1]
             snake_case_	=				upsample_block(_UpperCAmelCase		,				res_hidden_states_tuple=_UpperCAmelCase		,				temb=_UpperCAmelCase )

       # 5. post-process
       if self.out_block:
             snake_case_	=				self.out_block(_UpperCAmelCase		,				_UpperCAmelCase )

       if not return_dict:
             return (sample,)

       return UNetaDOutput(sample=_UpperCAmelCase )



from __future__ import annotations

import time

UpperCAmelCase      =   list[tuple[int, int]]

UpperCAmelCase      =   [
    [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],
]

UpperCAmelCase      =   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				pos_x
       snake_case_	=				pos_y
       snake_case_	=				(pos_y, pos_x)
       snake_case_	=				goal_x
       snake_case_	=				goal_y
       snake_case_	=				parent







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				Node(start[1]		,				start[0]		,				goal[1]		,				goal[0]		,				_UpperCAmelCase )
       snake_case_	=				Node(goal[1]		,				goal[0]		,				goal[1]		,				goal[0]		,				_UpperCAmelCase )

       snake_case_	=				[self.start]
       snake_case_	=				False



 def 						UpperCamelCase__					(     self ):
       while self.node_queue:
             snake_case_	=				self.node_queue.pop(0 )

             if current_node.pos == self.target.pos:
                   snake_case_	=				True
                   return self.retrace_path(_UpperCAmelCase )

             snake_case_	=				self.get_successors(_UpperCAmelCase )

             for node in successors:
                   self.node_queue.append(_UpperCAmelCase )

       if not self.reached:
             return [self.start.pos]
       return None



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				[]
       for action in delta:
             snake_case_	=				parent.pos_x + action[1]
             snake_case_	=				parent.pos_y + action[0]
             if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_UpperCAmelCase ) - 1):
                   continue

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

             successors.append(
                 Node(_UpperCAmelCase		,				_UpperCAmelCase		,				self.target.pos_y		,				self.target.pos_x		,				_UpperCAmelCase ) )
       return successors





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				node
       snake_case_	=				[]
       while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x) )
             snake_case_	=				current_node.parent
       path.reverse()
       return path







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				BreadthFirstSearch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				BreadthFirstSearch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				False



 def 						UpperCamelCase__					(     self ):
       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
             snake_case_	=				self.fwd_bfs.node_queue.pop(0 )
             snake_case_	=				self.bwd_bfs.node_queue.pop(0 )

             if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case_	=				True
                   return self.retrace_bidirectional_path(
                       _UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				current_bwd_node
             snake_case_	=				current_fwd_node

             snake_case_	=				{
                 self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCAmelCase ),
                 self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCAmelCase ),
             }

             for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                         bfs.node_queue.append(_UpperCAmelCase )

       if not self.reached:
             return [self.fwd_bfs.start.pos]
       return None





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.fwd_bfs.retrace_path(_UpperCAmelCase )
       snake_case_	=				self.bwd_bfs.retrace_path(_UpperCAmelCase )
       bwd_path.pop()
       bwd_path.reverse()
       snake_case_	=				fwd_path + bwd_path
       return path


if __name__ == "__main__":
   # all coordinates are given in format [y,x]
   import doctest

   doctest.testmod()
   UpperCAmelCase      =   (0, 0)
   UpperCAmelCase      =   (len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   UpperCAmelCase      =   time.time()
   UpperCAmelCase      =   BreadthFirstSearch(init, goal)
   UpperCAmelCase      =   bfs.search()
   UpperCAmelCase      =   time.time() - start_bfs_time

   print("""Unidirectional BFS computation time : """, bfs_time)

   UpperCAmelCase      =   time.time()
   UpperCAmelCase      =   BidirectionalBreadthFirstSearch(init, goal)
   UpperCAmelCase      =   bd_bfs.search()
   UpperCAmelCase      =   time.time() - start_bd_bfs_time

   print("""Bidirectional BFS computation time : """, bd_bfs_time)