

"""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 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Node | None	):
    __lowercase								=						pos_x
    __lowercase								=						pos_y
    __lowercase								=						(pos_y, pos_x)
    __lowercase								=						goal_x
    __lowercase								=						goal_y
    __lowercase								=						parent







class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			str,					UpperCAmelCase__							:			tuple[int, int],					UpperCAmelCase__							:			tuple[int, int]	):
    __lowercase								=						Node(start[1],					start[0],					goal[1],					goal[0],					UpperCAmelCase__	)
    __lowercase								=						Node(goal[1],					goal[0],					goal[1],					goal[0],					UpperCAmelCase__	)

    __lowercase								=						[self.start]
    __lowercase								=						False


 def 					_lowercase (			self							:			Optional[int]	):
    while self.node_queue:
       __lowercase								=						self.node_queue.pop(0	)

       if current_node.pos == self.target.pos:
          __lowercase								=						True
          return self.retrace_path(UpperCAmelCase__	)

       __lowercase								=						self.get_successors(UpperCAmelCase__	)

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

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


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			Node	):
    __lowercase								=						[]
    for action in delta:
       __lowercase								=						parent.pos_x + action[1]
       __lowercase								=						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 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Node | None	):
    __lowercase								=						node
    __lowercase								=						[]
    while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)	)
       __lowercase								=						current_node.parent
    path.reverse()
    return path







class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						BreadthFirstSearch(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						BreadthFirstSearch(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						False


 def 					_lowercase (			self							:			List[Any]	):
    while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
       __lowercase								=						self.fwd_bfs.node_queue.pop(0	)
       __lowercase								=						self.bwd_bfs.node_queue.pop(0	)

       if current_bwd_node.pos == current_fwd_node.pos:
          __lowercase								=						True
          return self.retrace_bidirectional_path(
              UpperCAmelCase__,					UpperCAmelCase__	)

       __lowercase								=						current_bwd_node
       __lowercase								=						current_fwd_node

       __lowercase								=						{
           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 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Node,					UpperCAmelCase__							:			Node	):
    __lowercase								=						self.fwd_bfs.retrace_path(UpperCAmelCase__	)
    __lowercase								=						self.bwd_bfs.retrace_path(UpperCAmelCase__	)
    bwd_path.pop()
    bwd_path.reverse()
    __lowercase								=						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)








import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)
										processor.save_pretrained(
										    self.tmpdirname			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			speaker_embeddings_directory=self.speaker_embeddings_directory			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		BarkProcessor.from_pretrained(
										    self.tmpdirname			,			self.speaker_embeddings_dict_path			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			)

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


				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''semantic_prompt''': np.ones(lowerCAmelCase__	),
										    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)	),
										    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)	),
										}

										# test providing already loaded voice_preset
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		inputs['''history_prompt''']
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from npz file
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inputs['''history_prompt''']

										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from the hub
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer(
										    self.input_string			,			padding='''max_length'''			,			max_length=256			,			add_special_tokens=lowerCAmelCase__			,			return_attention_mask=lowerCAmelCase__			,			return_token_type_ids=lowerCAmelCase__			,			)

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