






from __future__ import annotations

import time

lowerCamelCase      :       List[str]    =						list[tuple[int, int]]

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



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       pos_x
           __lowerCAmelCase             =       pos_y
           __lowerCAmelCase             =       (pos_y, pos_x)
           __lowerCAmelCase             =       goal_x
           __lowerCAmelCase             =       goal_y
           __lowerCAmelCase             =       parent



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Union[str, Any]:
           __lowerCAmelCase             =       Node(start[1]       ,    start[0]       ,    goal[1]       ,    goal[0]       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       Node(goal[1]       ,    goal[0]       ,    goal[1]       ,    goal[0]       ,    __UpperCamelCase						)

           __lowerCAmelCase             =       [self.start]
           __lowerCAmelCase             =       False


      def 			__UpperCAmelCase		(						self						)->						Path | None:
           while self.node_queue:
                __lowerCAmelCase             =       self.node_queue.pop(0						)

                if current_node.pos == self.target.pos:
                     __lowerCAmelCase             =       True
                     return self.retrace_path(__UpperCamelCase						)

                __lowerCAmelCase             =       self.get_successors(__UpperCamelCase						)

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

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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						list[Node]:
           __lowerCAmelCase             =       []
           for action in delta:
                __lowerCAmelCase             =       parent.pos_x + action[1]
                __lowerCAmelCase             =       parent.pos_y + action[0]
                if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(__UpperCamelCase						) - 1):
                     continue

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

                successors.append(
                    Node(__UpperCamelCase       ,    __UpperCamelCase       ,    self.target.pos_y       ,    self.target.pos_x       ,    __UpperCamelCase						)						)
           return successors


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Path:
           __lowerCAmelCase             =       node
           __lowerCAmelCase             =       []
           while current_node is not None:
                path.append((current_node.pos_y, current_node.pos_x)						)
                __lowerCAmelCase             =       current_node.parent
           path.reverse()
           return path



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       BreadthFirstSearch(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       BreadthFirstSearch(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       False


      def 			__UpperCAmelCase		(						self						)->						Path | None:
           while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
                __lowerCAmelCase             =       self.fwd_bfs.node_queue.pop(0						)
                __lowerCAmelCase             =       self.bwd_bfs.node_queue.pop(0						)

                if current_bwd_node.pos == current_fwd_node.pos:
                     __lowerCAmelCase             =       True
                     return self.retrace_bidirectional_path(
                         __UpperCamelCase       ,    __UpperCamelCase						)

                __lowerCAmelCase             =       current_bwd_node
                __lowerCAmelCase             =       current_fwd_node

                __lowerCAmelCase             =       {
                    self.fwd_bfs: self.fwd_bfs.get_successors(__UpperCamelCase						),
                    self.bwd_bfs: self.bwd_bfs.get_successors(__UpperCamelCase						),
                }

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

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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Path:
           __lowerCAmelCase             =       self.fwd_bfs.retrace_path(__UpperCamelCase						)
           __lowerCAmelCase             =       self.bwd_bfs.retrace_path(__UpperCamelCase						)
           bwd_path.pop()
           bwd_path.reverse()
           __lowerCAmelCase             =       fwd_path + bwd_path
           return path


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

 doctest.testmod()
 lowerCamelCase      :       Tuple    =						(0, 0)
 lowerCamelCase      :       List[Any]    =						(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 lowerCamelCase      :       List[str]    =						time.time()
 lowerCamelCase      :       Optional[int]    =						BreadthFirstSearch(init, goal)
 lowerCamelCase      :       Dict    =						bfs.search()
 lowerCamelCase      :       Any    =						time.time() - start_bfs_time

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

 lowerCamelCase      :       str    =						time.time()
 lowerCamelCase      :       Union[str, Any]    =						BidirectionalBreadthFirstSearch(init, goal)
 lowerCamelCase      :       Any    =						bd_bfs.search()
 lowerCamelCase      :       Optional[int]    =						time.time() - start_bd_bfs_time

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














import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       ClapProcessor.from_pretrained(self.tmpdirname						)

           self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()						)
           self.assertIsInstance(processor.tokenizer       ,    __UpperCamelCase						)

           self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ClapProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

           __lowerCAmelCase             =       self.get_tokenizer(bos_token="(BOS)"       ,    eos_token="(EOS)"						)
           __lowerCAmelCase             =       self.get_feature_extractor(do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

           __lowerCAmelCase             =       ClapProcessor.from_pretrained(
               self.tmpdirname       ,    bos_token="(BOS)"       ,    eos_token="(EOS)"       ,    do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

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

           self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor_add_kwargs.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processor(audios=__UpperCamelCase       ,    return_tensors="np"						)

           for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           self.assertListEqual(
               processor.model_input_names[2:]       ,    feature_extractor.model_input_names       ,    msg="`processor` and `feature_extractor` model input names do not match"       ,    )






