'''simple docstring'''




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_A								=   0

_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

_A								=   tuple[int, int]


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     None:
        lowercase_					:       List[Any]     				=					pos_x
        lowercase_					:       str     				=					pos_y
        lowercase_					:       List[str]     				=					(pos_y, pos_x)
        lowercase_					:       Optional[Any]     				=					goal_x
        lowercase_					:       Any     				=					goal_y
        lowercase_					:       str     				=					g_cost
        lowercase_					:       Union[str, Any]     				=					parent
        lowercase_					:       str     				=					self.calculate_heuristic()
        lowercase_					:       Dict     				=					self.g_cost + self.h_cost
   def 		_lowerCamelCase (self				)    ->     float:
        lowercase_					:       List[Any]     				=					self.pos_x - self.goal_x
        lowercase_					:       str     				=					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:
        return self.f_cost < other.f_cost



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       Tuple     				=					Node(start[1]       ,				start[0]       ,				goal[1]       ,				goal[0]       ,				0       ,				_a				)
        lowercase_					:       Tuple     				=					Node(goal[1]       ,				goal[0]       ,				goal[1]       ,				goal[0]       ,				99_999       ,				_a				)

        lowercase_					:       Dict     				=					[self.start]
        lowercase_					:       list[Node]     				=					[]

        lowercase_					:       Union[str, Any]     				=					False
   def 		_lowerCamelCase (self				)    ->     list[TPosition]:
        while self.open_nodes:
             # Open Nodes are sorted using __lt__
             self.open_nodes.sort()
             lowercase_					:       Any     				=					self.open_nodes.pop(0				)

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

             self.closed_nodes.append(_a				)
             lowercase_					:       Optional[Any]     				=					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
                       lowercase_					:       Dict     				=					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 		_lowerCamelCase (self       ,				_a				)    ->     list[Node]:
        lowercase_					:       Tuple     				=					[]
        for action in delta:
             lowercase_					:       str     				=					parent.pos_x + action[1]
             lowercase_					:       Dict     				=					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 		_lowerCamelCase (self       ,				_a				)    ->     list[TPosition]:
        lowercase_					:       Any     				=					node
        lowercase_					:       List[Any]     				=					[]
        while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x)				)
             lowercase_					:       str     				=					current_node.parent
        path.reverse()
        return path



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a				)    ->     None:
        lowercase_					:       List[Any]     				=					AStar(_a       ,				_a				)
        lowercase_					:       Tuple     				=					AStar(_a       ,				_a				)
        lowercase_					:       Union[str, Any]     				=					False
   def 		_lowerCamelCase (self				)    ->     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()
             lowercase_					:       Any     				=					self.fwd_astar.open_nodes.pop(0				)
             lowercase_					:       str     				=					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				)

             lowercase_					:       List[str]     				=					current_bwd_node
             lowercase_					:       str     				=					current_fwd_node

             lowercase_					:       int     				=					{
                 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
                            lowercase_					:       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 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     list[TPosition]:
        lowercase_					:       List[str]     				=					self.fwd_astar.retrace_path(_a				)
        lowercase_					:       Dict     				=					self.bwd_astar.retrace_path(_a				)
        bwd_path.pop()
        bwd_path.reverse()
        lowercase_					:       Dict     				=					fwd_path + bwd_path
        return path


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

 _A								=   time.time()
 _A								=   AStar(init, goal)
 _A								=   a_star.search()
 _A								=   time.time() - start_time
 print(F"""AStar execution time = {end_time:f} seconds""")

 _A								=   time.time()
 _A								=   BidirectionalAStar(init, goal)
 _A								=   time.time() - bd_start_time
 print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=12       ,				_a=7       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=99       ,				_a=32       ,				_a=32       ,				_a=2       ,				_a=4       ,				_a=37       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=0.02       ,				_a=0       ,				_a=None       ,				)    ->     Optional[Any]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       List[Any]     				=					batch_size
        lowercase_					:       Optional[Any]     				=					seq_length
        lowercase_					:       Optional[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_input_mask
        lowercase_					:       Any     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					projection_dim
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					dropout
        lowercase_					:       Tuple     				=					attention_dropout
        lowercase_					:       Optional[Any]     				=					max_position_embeddings
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					scope
        lowercase_					:       Optional[Any]     				=					bos_token_id
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_input_mask:
             lowercase_					:       Dict     				=					random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
             lowercase_					:       Dict     				=					input_mask.numpy()
             lowercase_				,lowercase_					:       int     				=					input_mask.shape
             lowercase_					:       int     				=					np.random.randint(1       ,				seq_length - 1       ,				size=(batch_size,)				)
             for batch_idx, start_index in enumerate(_a				):
                  lowercase_					:       Tuple     				=					1
                  lowercase_					:       List[Any]     				=					0

        lowercase_					:       List[Any]     				=					self.get_config()

        return config, input_ids, tf.convert_to_tensor(_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        return BlipTextConfig(
            vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				projection_dim=self.projection_dim       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				dropout=self.dropout       ,				attention_dropout=self.attention_dropout       ,				max_position_embeddings=self.max_position_embeddings       ,				initializer_range=self.initializer_range       ,				bos_token_id=self.bos_token_id       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFBlipTextModel(config=_a				)
        lowercase_					:       List[str]     				=					model(_a       ,				attention_mask=_a       ,				training=_a				)
        lowercase_					:       Union[str, Any]     				=					model(_a       ,				training=_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
        self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)				)





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Any     =    (TFBlipTextModel,) if is_tf_available() else ()
   A							:       Union[str, Any]     =    False
   A							:       int     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					BlipTextModelTester(self				)
        lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        pass
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='Blip does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[str]     				=					TFBlipTextModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)





   def 		_lowerCamelCase (self       ,				_a=True				)    ->     Optional[int]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=_a				)




