



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import (
			    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    FlaubertConfig,
			    TFFlaubertForMultipleChoice,
			    TFFlaubertForQuestionAnsweringSimple,
			    TFFlaubertForSequenceClassification,
			    TFFlaubertForTokenClassification,
			    TFFlaubertModel,
			    TFFlaubertWithLMHeadModel,
			)


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Any       ,	snake_case    :				int       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				parent
											SCREAMING_SNAKE_CASE							:     str						=				13
											SCREAMING_SNAKE_CASE							:     Dict						=				7
											SCREAMING_SNAKE_CASE							:     Tuple						=				True
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				True
											SCREAMING_SNAKE_CASE							:     List[Any]						=				True
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				True
											SCREAMING_SNAKE_CASE							:     Any						=				True
											SCREAMING_SNAKE_CASE							:     Dict						=				False
											SCREAMING_SNAKE_CASE							:     Any						=				False
											SCREAMING_SNAKE_CASE							:     Tuple						=				False
											SCREAMING_SNAKE_CASE							:     Dict						=				2
											SCREAMING_SNAKE_CASE							:     List[str]						=				99
											SCREAMING_SNAKE_CASE							:     int						=				0
											SCREAMING_SNAKE_CASE							:     List[Any]						=				32
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				2
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				4
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				0.1
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				0.1
											SCREAMING_SNAKE_CASE							:     List[Any]						=				512
											SCREAMING_SNAKE_CASE							:     int						=				16
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				2
											SCREAMING_SNAKE_CASE							:     str						=				0.02
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				3
											SCREAMING_SNAKE_CASE							:     str						=				4
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'last'
											SCREAMING_SNAKE_CASE							:     Dict						=				True
											SCREAMING_SNAKE_CASE							:     Tuple						=				None
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				0







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size	)
											SCREAMING_SNAKE_CASE							:     int						=				random_attention_mask([self.batch_size, self.seq_length]       ,	dtype=tf.floataa	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				None
											if self.use_input_lengths:
																SCREAMING_SNAKE_CASE							:     Tuple						=				(
																    ids_tensor([self.batch_size]       ,	vocab_size=2	) + self.seq_length - 2
																)  # small variation of seq_length

											SCREAMING_SNAKE_CASE							:     int						=				None
											if self.use_token_type_ids:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				ids_tensor([self.batch_size, self.seq_length]       ,	self.n_langs	)

											SCREAMING_SNAKE_CASE							:     int						=				None
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				None
											SCREAMING_SNAKE_CASE							:     Any						=				None
											if self.use_labels:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				ids_tensor([self.batch_size]       ,	self.type_sequence_label_size	)
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels	)
																SCREAMING_SNAKE_CASE							:     int						=				ids_tensor([self.batch_size]       ,	2       ,	dtype=tf.floataa	)
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				ids_tensor([self.batch_size]       ,	self.num_choices	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				FlaubertConfig(
											    vocab_size=self.vocab_size       ,	n_special=self.n_special       ,	emb_dim=self.hidden_size       ,	n_layers=self.num_hidden_layers       ,	n_heads=self.num_attention_heads       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	gelu_activation=self.gelu_activation       ,	sinusoidal_embeddings=self.sinusoidal_embeddings       ,	asm=self.asm       ,	causal=self.causal       ,	n_langs=self.n_langs       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	summary_type=self.summary_type       ,	use_proj=self.use_proj       ,	bos_token_id=self.bos_token_id       ,	)

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				Any       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				int       ,	snake_case    :				str       ,	snake_case    :				str       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				Tuple       ,	snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				TFFlaubertModel(config=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
											SCREAMING_SNAKE_CASE							:     Dict						=				model(snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				[input_ids, input_mask]
											SCREAMING_SNAKE_CASE							:     Tuple						=				model(snake_case	)
											self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)	)







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				List[Any]       ,	snake_case    :				Any       ,	snake_case    :				Tuple       ,	snake_case    :				str       ,	snake_case    :				List[Any]       ,	snake_case    :				Dict       ,	snake_case    :				Tuple       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				List[Any]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				TFFlaubertWithLMHeadModel(snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
											SCREAMING_SNAKE_CASE							:     int						=				model(snake_case	)

											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)	)







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Tuple       ,	snake_case    :				List[Any]       ,	snake_case    :				List[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				Any       ,	snake_case    :				Dict       ,	snake_case    :				List[str]       ,	snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				TFFlaubertForQuestionAnsweringSimple(snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				{'input_ids': input_ids, 'lengths': input_lengths}

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				model(snake_case	)

											self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length)	)
											self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length)	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				List[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				Dict       ,	snake_case    :				List[str]       ,	snake_case    :				Any       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Any       ,	snake_case    :				Dict       ,	snake_case    :				Any       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				TFFlaubertForSequenceClassification(snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{'input_ids': input_ids, 'lengths': input_lengths}

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				model(snake_case	)

											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)	)







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				int       ,	snake_case    :				str       ,	snake_case    :				List[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				List[str]       ,	snake_case    :				Optional[Any]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.num_labels
											SCREAMING_SNAKE_CASE							:     Dict						=				TFFlaubertForTokenClassification(config=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
											SCREAMING_SNAKE_CASE							:     Tuple						=				model(snake_case	)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)	)







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				List[Any]       ,	snake_case    :				str       ,	snake_case    :				Optional[int]       ,	snake_case    :				Any       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Any       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				List[Any]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.num_choices
											SCREAMING_SNAKE_CASE							:     List[str]						=				TFFlaubertForMultipleChoice(config=snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				tf.tile(tf.expand_dims(snake_case       ,	1	)       ,	(1, self.num_choices, 1)	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				tf.tile(tf.expand_dims(snake_case       ,	1	)       ,	(1, self.num_choices, 1)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				tf.tile(tf.expand_dims(snake_case       ,	1	)       ,	(1, self.num_choices, 1)	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				{
											    'input_ids': multiple_choice_inputs_ids,
											    'attention_mask': multiple_choice_input_mask,
											    'token_type_ids': multiple_choice_token_type_ids,
											}
											SCREAMING_SNAKE_CASE							:     int						=				model(snake_case	)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_choices)	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.prepare_config_and_inputs()
											(
											    (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  (
											    SCREAMING_SNAKE_CASE
											)					,  
											)							:     Union[str, Any]						=				config_and_inputs
											SCREAMING_SNAKE_CASE							:     Dict						=				{
											    'input_ids': input_ids,
											    'token_type_ids': token_type_ids,
											    'langs': token_type_ids,
											    'lengths': input_lengths,
											}
											return config, inputs_dict


@require_tf
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		(
						    (
						        TFFlaubertModel,
						        TFFlaubertWithLMHeadModel,
						        TFFlaubertForSequenceClassification,
						        TFFlaubertForQuestionAnsweringSimple,
						        TFFlaubertForTokenClassification,
						        TFFlaubertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase			:		Optional[Any]		=		(
						    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						UpperCAmelCase			:		Tuple		=		(
						    {
						        'feature-extraction': TFFlaubertModel,
						        'fill-mask': TFFlaubertWithLMHeadModel,
						        'question-answering': TFFlaubertForQuestionAnsweringSimple,
						        'text-classification': TFFlaubertForSequenceClassification,
						        'token-classification': TFFlaubertForTokenClassification,
						        'zero-shot': TFFlaubertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCAmelCase			:		str		=		False
						UpperCAmelCase			:		str		=		False







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Any       ,	snake_case    :				Dict       ,	snake_case    :				int       ,	snake_case    :				str       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith('Fast'	)
											):
																# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																return True

											return False







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				TFFlaubertModelTester(self	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				ConfigTester(self       ,	config_class=snake_case       ,	emb_dim=37	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											self.config_tester.run_common_tests()







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_flaubert_model(*snake_case	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_flaubert_lm_head(*snake_case	)







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_flaubert_qa(*snake_case	)







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_flaubert_for_token_classification(*snake_case	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_flaubert_for_multiple_choice(*snake_case	)







						@slow
						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																SCREAMING_SNAKE_CASE							:     List[str]						=				TFFlaubertModel.from_pretrained(snake_case	)
																self.assertIsNotNone(snake_case	)


@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						@slow
						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'	)

											SCREAMING_SNAKE_CASE							:     str						=				tf.convert_to_tensor(
											    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]       ,	dtype=tf.intaa       ,	)  # "J'aime flaubert !"

											SCREAMING_SNAKE_CASE							:     List[Any]						=				model(snake_case	)[0]
											SCREAMING_SNAKE_CASE							:     int						=				tf.TensorShape((1, 8, 512)	)
											self.assertEqual(output.shape       ,	snake_case	)
											# compare the actual values for a slice.
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				tf.convert_to_tensor(
											    [
											        [
											            [-1.8768773, -1.566555, 0.27072418],
											            [-1.6920038, -0.5873505, 1.9329599],
											            [-2.9563985, -1.6993835, 1.7972052],
											        ]
											    ]       ,	dtype=tf.floataa       ,	)

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,	expected_slice.numpy()       ,	atol=1E-4	)	)




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_lowerCamelCase    :		List[str]		      =    0

_lowerCamelCase    :		Tuple		      =    [
    [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],
]

_lowerCamelCase    :		Tuple		      =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_lowerCamelCase    :		Union[str, Any]		      =    tuple[int, int]


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				Node | None       ,	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.pos_x - self.goal_x
											SCREAMING_SNAKE_CASE							:     str						=				self.pos_y - self.goal_y
											if HEURISTIC == 1:
																return abs(snake_case	) + abs(snake_case	)
											else:
																return sqrt(dy**2 + dx**2	)







						def __lt__(  self    :				Any       ,	snake_case    :				Node	):



											'''simple docstring'''


											return self.f_cost < other.f_cost


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				TPosition       ,	snake_case    :				TPosition	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Node(start[1]       ,	start[0]       ,	goal[1]       ,	goal[0]       ,	0       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				Node(goal[1]       ,	goal[0]       ,	goal[1]       ,	goal[0]       ,	99999       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				[self.start]
											SCREAMING_SNAKE_CASE							:     list[Node]						=				[]

											SCREAMING_SNAKE_CASE							:     int						=				False







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											while self.open_nodes:
																# Open Nodes are sorted using __lt__
																self.open_nodes.sort()
																SCREAMING_SNAKE_CASE							:     Dict						=				self.open_nodes.pop(0	)

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

																self.closed_nodes.append(snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				self.get_successors(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(snake_case	)
																					else:
																										# retrieve the best current path
																										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.open_nodes.pop(self.open_nodes.index(snake_case	)	)

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

											return [self.start.pos]







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Node	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				[]
											for action in delta:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				parent.pos_x + action[1]
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				parent.pos_y + action[0]
																if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(snake_case	) - 1):
																					continue

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

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







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Node | None	):



											'''simple docstring'''


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


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				TPosition       ,	snake_case    :				TPosition	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				AStar(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				AStar(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				False







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''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							:     Dict						=				self.fwd_astar.open_nodes.pop(0	)
																SCREAMING_SNAKE_CASE							:     Any						=				self.bwd_astar.open_nodes.pop(0	)

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

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

																SCREAMING_SNAKE_CASE							:     str						=				current_bwd_node
																SCREAMING_SNAKE_CASE							:     int						=				current_fwd_node

																SCREAMING_SNAKE_CASE							:     str						=				{
																    self.fwd_astar: self.fwd_astar.get_successors(snake_case	),
																    self.bwd_astar: self.bwd_astar.get_successors(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(snake_case	)
																										else:
																															# retrieve the best current path
																															SCREAMING_SNAKE_CASE							:     List[Any]						=				astar.open_nodes.pop(
																															    astar.open_nodes.index(snake_case	)	)

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

											return [self.fwd_astar.start.pos]







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Node       ,	snake_case    :				Node	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.fwd_astar.retrace_path(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.bwd_astar.retrace_path(snake_case	)
											bwd_path.pop()
											bwd_path.reverse()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				fwd_path + bwd_path
											return path


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

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

			_lowerCamelCase    :		Optional[Any]		      =    time.time()
			_lowerCamelCase    :		Union[str, Any]		      =    BidirectionalAStar(init, goal)
			_lowerCamelCase    :		List[Any]		      =    time.time() - bd_start_time
			print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")