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						:



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	pos_x
            _A=	pos_y
            _A=	(pos_y, pos_x)
            _A=	goal_x
            _A=	goal_y
            _A=	parent





class      lowerCAmelCase						:



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	Node(start[1]				,      start[0]				,      goal[1]				,      goal[0]				,      _SCREAMING_SNAKE_CASE       )
            _A=	Node(goal[1]				,      goal[0]				,      goal[1]				,      goal[0]				,      _SCREAMING_SNAKE_CASE       )

            _A=	[self.start]
            _A=	False



     def 			a__  (					self       ):
            while self.node_queue:
                   _A=	self.node_queue.pop(0       )

                   if current_node.pos == self.target.pos:
                          _A=	True
                          return self.retrace_path(_SCREAMING_SNAKE_CASE       )

                   _A=	self.get_successors(_SCREAMING_SNAKE_CASE       )

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

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



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	[]
            for action in delta:
                   _A=	parent.pos_x + action[1]
                   _A=	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				,      _SCREAMING_SNAKE_CASE       )       )
            return successors





     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	node
            _A=	[]
            while current_node is not None:
                   path.append((current_node.pos_y, current_node.pos_x)       )
                   _A=	current_node.parent
            path.reverse()
            return path





class      lowerCAmelCase						:



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	BreadthFirstSearch(_SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE       )
            _A=	BreadthFirstSearch(_SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE       )
            _A=	False



     def 			a__  (					self       ):
            while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
                   _A=	self.fwd_bfs.node_queue.pop(0       )
                   _A=	self.bwd_bfs.node_queue.pop(0       )

                   if current_bwd_node.pos == current_fwd_node.pos:
                          _A=	True
                          return self.retrace_bidirectional_path(
                              _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE       )

                   _A=	current_bwd_node
                   _A=	current_fwd_node

                   _A=	{
                       self.fwd_bfs: self.fwd_bfs.get_successors(_SCREAMING_SNAKE_CASE       ),
                       self.bwd_bfs: self.bwd_bfs.get_successors(_SCREAMING_SNAKE_CASE       ),
                   }

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

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





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.fwd_bfs.retrace_path(_SCREAMING_SNAKE_CASE       )
            _A=	self.bwd_bfs.retrace_path(_SCREAMING_SNAKE_CASE       )
            bwd_path.pop()
            bwd_path.reverse()
            _A=	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)
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )