'''simple docstring'''




from __future__ import annotations

import time

_A								=   list[tuple[int, int]]

_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


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a				)    ->     List[str]:
        lowercase_					:       Any     				=					pos_x
        lowercase_					:       List[str]     				=					pos_y
        lowercase_					:       Union[str, Any]     				=					(pos_y, pos_x)
        lowercase_					:       Optional[int]     				=					goal_x
        lowercase_					:       List[str]     				=					goal_y
        lowercase_					:       str     				=					parent



class       UpperCAmelCase__  :






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

        lowercase_					:       int     				=					[self.start]
        lowercase_					:       Union[str, Any]     				=					False
   def 		_lowerCamelCase (self				)    ->     Path | None:
        while self.node_queue:
             lowercase_					:       List[Any]     				=					self.node_queue.pop(0				)

             if current_node.pos == self.target.pos:
                  lowercase_					:       List[Any]     				=					True
                  return self.retrace_path(_a				)

             lowercase_					:       Any     				=					self.get_successors(_a				)

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

        if not self.reached:
             return [self.start.pos]
        return None
   def 		_lowerCamelCase (self       ,				_a				)    ->     list[Node]:
        lowercase_					:       Tuple     				=					[]
        for action in delta:
             lowercase_					:       str     				=					parent.pos_x + action[1]
             lowercase_					:       Union[str, Any]     				=					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       ,				_a				)				)
        return successors





   def 		_lowerCamelCase (self       ,				_a				)    ->     Path:
        lowercase_					:       Optional[Any]     				=					node
        lowercase_					:       str     				=					[]
        while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x)				)
             lowercase_					:       Optional[Any]     				=					current_node.parent
        path.reverse()
        return path



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       Tuple     				=					BreadthFirstSearch(_a       ,				_a				)
        lowercase_					:       Union[str, Any]     				=					BreadthFirstSearch(_a       ,				_a				)
        lowercase_					:       int     				=					False
   def 		_lowerCamelCase (self				)    ->     Path | None:
        while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
             lowercase_					:       Union[str, Any]     				=					self.fwd_bfs.node_queue.pop(0				)
             lowercase_					:       Union[str, Any]     				=					self.bwd_bfs.node_queue.pop(0				)

             if current_bwd_node.pos == current_fwd_node.pos:
                  lowercase_					:       Optional[Any]     				=					True
                  return self.retrace_bidirectional_path(
                      _a       ,				_a				)

             lowercase_					:       Union[str, Any]     				=					current_bwd_node
             lowercase_					:       List[str]     				=					current_fwd_node

             lowercase_					:       int     				=					{
                 self.fwd_bfs: self.fwd_bfs.get_successors(_a				),
                 self.bwd_bfs: self.bwd_bfs.get_successors(_a				),
             }

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

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





   def 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     Path:
        lowercase_					:       List[str]     				=					self.fwd_bfs.retrace_path(_a				)
        lowercase_					:       Any     				=					self.bwd_bfs.retrace_path(_a				)
        bwd_path.pop()
        bwd_path.reverse()
        lowercase_					:       Union[str, Any]     				=					fwd_path + bwd_path
        return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 _A								=   (0, 0)
 _A								=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _A								=   time.time()
 _A								=   BreadthFirstSearch(init, goal)
 _A								=   bfs.search()
 _A								=   time.time() - start_bfs_time

 print('Unidirectional BFS computation time : ', bfs_time)

 _A								=   time.time()
 _A								=   BidirectionalBreadthFirstSearch(init, goal)
 _A								=   bd_bfs.search()
 _A								=   time.time() - start_bd_bfs_time

 print('Bidirectional BFS computation time : ', bd_bfs_time)





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, Any]     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        lowercase_					:       List[str]     				=					os.path.join(self.tmpdirname       ,				_a				)
        with open(self.image_processor_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             json.dump(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       Any     				=					AlignProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()       ,				tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        self.assertEqual(processor_slow.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer       ,				_a				)

        self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					processor(images=_a       ,				return_tensors='np'				)

        for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()       ,				input_processor[key].sum()       ,				delta=1e-2				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']				)

        # test if it raises when no input is passed
        with pytest.raises(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				processor.model_input_names				)




