'''simple docstring'''
from __future__ import annotations

import time

a=  list[tuple[int, int]]

a=  [
    [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],
]

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






class 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  pos_x
         __UpperCamelCase	:	Union[str, Any]		    =  pos_y
         __UpperCamelCase	:	Dict		    =  (pos_y, pos_x)
         __UpperCamelCase	:	Optional[int]		    =  goal_x
         __UpperCamelCase	:	Tuple		    =  goal_y
         __UpperCamelCase	:	int		    =  parent






class 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  Node(start[1]     ,			start[0]     ,			goal[1]     ,			goal[0]     ,			_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  Node(goal[1]     ,			goal[0]     ,			goal[1]     ,			goal[0]     ,			_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  [self.start]
         __UpperCamelCase	:	Union[str, Any]		    =  False







     def lowerCAmelCase     (		self    ):
         while self.node_queue:
             __UpperCamelCase	:	Optional[Any]		    =  self.node_queue.pop(0    )

             if current_node.pos == self.target.pos:
                 __UpperCamelCase	:	Union[str, Any]		    =  True
                 return self.retrace_path(_lowerCamelCase    )

             __UpperCamelCase	:	Tuple		    =  self.get_successors(_lowerCamelCase    )

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

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         for action in delta:
             __UpperCamelCase	:	int		    =  parent.pos_x + action[1]
             __UpperCamelCase	:	int		    =  parent.pos_y + action[0]
             if not (0 <= pos_x <= len(grid[0]    ) - 1 and 0 <= pos_y <= len(_lowerCamelCase    ) - 1):
                 continue

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

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  node
         __UpperCamelCase	:	List[Any]		    =  []
         while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x)    )
             __UpperCamelCase	:	Optional[int]		    =  current_node.parent
         path.reverse()
         return path






class 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  BreadthFirstSearch(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  BreadthFirstSearch(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  False







     def lowerCAmelCase     (		self    ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
             __UpperCamelCase	:	List[str]		    =  self.fwd_bfs.node_queue.pop(0    )
             __UpperCamelCase	:	int		    =  self.bwd_bfs.node_queue.pop(0    )

             if current_bwd_node.pos == current_fwd_node.pos:
                 __UpperCamelCase	:	Tuple		    =  True
                 return self.retrace_bidirectional_path(
                     _lowerCamelCase     ,			_lowerCamelCase    )

             __UpperCamelCase	:	int		    =  current_bwd_node
             __UpperCamelCase	:	Union[str, Any]		    =  current_fwd_node

             __UpperCamelCase	:	Optional[int]		    =  {
                 self.fwd_bfs: self.fwd_bfs.get_successors(_lowerCamelCase    ),
                 self.bwd_bfs: self.bwd_bfs.get_successors(_lowerCamelCase    ),
             }

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

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  self.fwd_bfs.retrace_path(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.bwd_bfs.retrace_path(_lowerCamelCase    )
         bwd_path.pop()
         bwd_path.reverse()
         __UpperCamelCase	:	Dict		    =  fwd_path + bwd_path
         return path


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

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

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

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

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

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




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



