


'''simple docstring'''




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_a   :   Tuple												=   0

_a   :   str												=   [
    [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   :   List[Any]												=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_a   :   Any												=   tuple[int, int]




class 			_lowercase :
   def __init__(						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      Node | None       ,   ) ->							None:
        __snake_case           =						pos_x
        __snake_case           =						pos_y
        __snake_case           =						(pos_y, pos_x)
        __snake_case           =						goal_x
        __snake_case           =						goal_y
        __snake_case           =						g_cost
        __snake_case           =						parent
        __snake_case           =						self.calculate_heuristic()
        __snake_case           =						self.g_cost + self.h_cost
   def a  (						self       :      Optional[int]      ) ->							float:
        __snake_case           =						self.pos_x - self.goal_x
        __snake_case           =						self.pos_y - self.goal_y
        if HEURISTIC == 1:
             return abs(SCREAMING_SNAKE_CASE_      ) + abs(SCREAMING_SNAKE_CASE_      )
        else:
             return sqrt(dy**2 + dx**2      )





   def __lt__(						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Node      ) ->							bool:
        return self.f_cost < other.f_cost




class 			_lowercase :
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      TPosition       ,   SCREAMING_SNAKE_CASE_       :      TPosition      ) ->							List[str]:
        __snake_case           =						Node(start[1]       ,   start[0]       ,   goal[1]       ,   goal[0]       ,   0       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						Node(goal[1]       ,   goal[0]       ,   goal[1]       ,   goal[0]       ,   9_9999       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						[self.start]
        __snake_case           =						[]

        __snake_case           =						False
   def a  (						self       :      int      ) ->							list[TPosition]:
        while self.open_nodes:
             # Open Nodes are sorted using __lt__
             self.open_nodes.sort()
             __snake_case           =						self.open_nodes.pop(0      )

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

             self.closed_nodes.append(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						self.get_successors(SCREAMING_SNAKE_CASE_      )

             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(SCREAMING_SNAKE_CASE_      )
                  else:
                       # retrieve the best current path
                       __snake_case           =						self.open_nodes.pop(self.open_nodes.index(SCREAMING_SNAKE_CASE_      )      )

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

        return [self.start.pos]
   def a  (						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      Node      ) ->							list[Node]:
        __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(SCREAMING_SNAKE_CASE_      ) - 1):
                  continue

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

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





   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Node | None      ) ->							list[TPosition]:
        __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 			_lowercase :
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      TPosition       ,   SCREAMING_SNAKE_CASE_       :      TPosition      ) ->							None:
        __snake_case           =						AStar(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						AStar(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						False
   def a  (						self       :      Optional[int]      ) ->							list[TPosition]:
        while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
             self.fwd_astar.open_nodes.sort()
             self.bwd_astar.open_nodes.sort()
             __snake_case           =						self.fwd_astar.open_nodes.pop(0      )
             __snake_case           =						self.bwd_astar.open_nodes.pop(0      )

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

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

             __snake_case           =						current_bwd_node
             __snake_case           =						current_fwd_node

             __snake_case           =						{
                 self.fwd_astar: self.fwd_astar.get_successors(SCREAMING_SNAKE_CASE_      ),
                 self.bwd_astar: self.bwd_astar.get_successors(SCREAMING_SNAKE_CASE_      ),
             }

             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(SCREAMING_SNAKE_CASE_      )
                       else:
                            # retrieve the best current path
                            __snake_case           =						astar.open_nodes.pop(
                                astar.open_nodes.index(SCREAMING_SNAKE_CASE_      )      )

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

        return [self.fwd_astar.start.pos]





   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Node       ,   SCREAMING_SNAKE_CASE_       :      Node      ) ->							list[TPosition]:
        __snake_case           =						self.fwd_astar.retrace_path(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						self.bwd_astar.retrace_path(SCREAMING_SNAKE_CASE_      )
        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]
       _a   :   Any												=   (0, 0)
       _a   :   Any												=   (len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       _a   :   Union[str, Any]												=   time.time()
       _a   :   Dict												=   AStar(init, goal)
       _a   :   Optional[int]												=   a_star.search()
       _a   :   int												=   time.time() - start_time
       print(f'''AStar execution time = {end_time:f} seconds''')

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












'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




