

"""simple docstring"""


from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
a					:Dict    = 0

a					:Any    = [
    [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					:Optional[int]    = [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

a					:int    = tuple[int, int]

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    )  ->      None:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      float:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.pos_x - self.goal_x
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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  )  ->      bool:


           """simple docstring"""


           return self.f_cost < other.f_cost





class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	Node(start[1]	,    start[0]	,    goal[1]	,    goal[0]	,    0	,    _a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	Node(goal[1]	,    goal[0]	,    goal[1]	,    goal[0]	,    99_999	,    _a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[self.start]
           SCREAMING_SNAKE_CASE__   :      list[Node]      		=	[]

           SCREAMING_SNAKE_CASE__   :      Any      		=	False

     def 					_a			(						self  )  ->      list[TPosition]:


           """simple docstring"""


           while self.open_nodes:
                 # Open Nodes are sorted using __lt__
                 self.open_nodes.sort()
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.open_nodes.pop(0  )

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

                 self.closed_nodes.append(_a  )
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	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
                             SCREAMING_SNAKE_CASE__   :      List[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 					_a			(						self	,    _a  )  ->      list[Node]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[]
           for action in delta:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	parent.pos_x + action[1]
                 SCREAMING_SNAKE_CASE__   :      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 					_a			(						self	,    _a  )  ->      list[TPosition]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	node
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[]
           while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)  )
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	current_node.parent
           path.reverse()
           return path





class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	AStar(_a	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	AStar(_a	,    _a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	False

     def 					_a			(						self  )  ->      list[TPosition]:


           """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()
                 SCREAMING_SNAKE_CASE__   :      Any      		=	self.fwd_astar.open_nodes.pop(0  )
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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  )

                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	current_bwd_node
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	current_fwd_node

                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
                     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
                                   SCREAMING_SNAKE_CASE__   :      List[str]      		=	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 					_a			(						self	,    _a	,    _a  )  ->      list[TPosition]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.fwd_astar.retrace_path(_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	self.bwd_astar.retrace_path(_a  )
           bwd_path.pop()
           bwd_path.reverse()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	fwd_path + bwd_path
           return path


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

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

  a					:Tuple    = time.time()
  a					:Any    = BidirectionalAStar(init, goal)
  a					:int    = time.time() - bd_start_time
  print(f'BidirectionalAStar execution time = {bd_end_time:f} seconds')







"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )

class      __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=True	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=16	,    _a=2	,    _a=0.02	,    _a=4	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	parent
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	use_attention_mask
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_token_type_ids
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      int      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      int      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	type_vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      int      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_choices

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_attention_mask:
                 SCREAMING_SNAKE_CASE__   :      int      		=	random_attention_mask([self.batch_size, self.seq_length]  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	None
           if self.use_token_type_ids:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	RoFormerConfig(
               vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=_a	,    initializer_range=self.initializer_range	,    )

           return config, input_ids, token_type_ids, attention_mask





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerModelTester(self  )





     @slow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,    from_pt=_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )





@require_flax
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	50_000

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(1, 6, vocab_size)
           self.assertEqual(output.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	jnp.array(
               [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]  )

           self.assertTrue(jnp.allclose(output[:, :3, :3]	,    _a	,    atol=1E-4  )  )




