

from __future__ import annotations

import time

lowercase_			=  list[tuple[int, int]]

lowercase_			=  [
    [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],
]

lowercase_			=  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right


class 			A						:







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	int,lowercase_			:	int,lowercase_			:	int,lowercase_			:	int,lowercase_			:	Node | None		)->   List[Any]:





        '''simple docstring'''







        A__   				=       pos_x
        A__   				=       pos_y
        A__   				=       (pos_y, pos_x)
        A__   				=       goal_x
        A__   				=       goal_y
        A__   				=       parent




class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	tuple[int, int],lowercase_			:	tuple[int, int]		)->   Tuple:





        '''simple docstring'''







        A__   				=       Node(start[1],start[0],goal[1],goal[0],lowercase_		)
        A__   				=       Node(goal[1],goal[0],goal[1],goal[0],lowercase_		)

        A__   				=       [self.start]
        A__   				=       False


    def 						snake_case__					(				self			:	int		)->   Path | None:





        '''simple docstring'''







        while self.node_queue:
            A__   				=       self.node_queue.pop(0		)

            if current_node.pos == self.target.pos:
                A__   				=       True
                return self.retrace_path(lowercase_		)

            A__   				=       self.get_successors(lowercase_		)

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

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


    def 						snake_case__					(				self			:	int,lowercase_			:	Node		)->   list[Node]:





        '''simple docstring'''







        A__   				=       []
        for action in delta:
            A__   				=       parent.pos_x + action[1]
            A__   				=       parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(lowercase_		) - 1):
                continue

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

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


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Node | None		)->   Path:





        '''simple docstring'''







        A__   				=       node
        A__   				=       []
        while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)		)
            A__   				=       current_node.parent
        path.reverse()
        return path




class 			A						:







    """simple docstring"""





    def __init__(				self			:	Tuple,lowercase_			:	Tuple,lowercase_			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       BreadthFirstSearch(lowercase_,lowercase_		)
        A__   				=       BreadthFirstSearch(lowercase_,lowercase_		)
        A__   				=       False


    def 						snake_case__					(				self			:	Tuple		)->   Path | None:





        '''simple docstring'''







        while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            A__   				=       self.fwd_bfs.node_queue.pop(0		)
            A__   				=       self.bwd_bfs.node_queue.pop(0		)

            if current_bwd_node.pos == current_fwd_node.pos:
                A__   				=       True
                return self.retrace_bidirectional_path(
                    lowercase_,lowercase_		)

            A__   				=       current_bwd_node
            A__   				=       current_fwd_node

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

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

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


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Node,lowercase_			:	Node		)->   Path:





        '''simple docstring'''







        A__   				=       self.fwd_bfs.retrace_path(lowercase_		)
        A__   				=       self.bwd_bfs.retrace_path(lowercase_		)
        bwd_path.pop()
        bwd_path.reverse()
        A__   				=       fwd_path + bwd_path
        return path


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

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

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

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

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

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




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str		)->   List[Any]:





        '''simple docstring'''







        for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['bs'],model_result['ss']		):
                A__   				=       model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(lowercase_		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sgugger/tiny-distilbert-classification'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    @unittest.skipIf(torch_device == 'cpu','Cant do half precision'		)
    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__   				=       None
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    @unittest.skipIf(torch_device == 'cpu','Can\'t do half precision'		)
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,save_to_csv=lowercase_,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(lowercase_,'inf_time.csv'		),train_memory_csv_file=os.path.join(lowercase_,'train_mem.csv'		),inference_memory_csv_file=os.path.join(lowercase_,'inf_mem.csv'		),train_time_csv_file=os.path.join(lowercase_,'train_time.csv'		),env_info_csv_file=os.path.join(lowercase_,'env.csv'		),multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            benchmark.run()
            self.assertTrue(Path(os.path.join(lowercase_,'inf_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'inf_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'env.csv'		)		).exists()		)


    def 						snake_case__					(				self			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_			:	Optional[Any]		):
            self.assertTrue(hasattr(lowercase_,'sequential'		)		)
            self.assertTrue(hasattr(lowercase_,'cumulative'		)		)
            self.assertTrue(hasattr(lowercase_,'current'		)		)
            self.assertTrue(hasattr(lowercase_,'total'		)		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],log_filename=os.path.join(lowercase_,'log.txt'		),log_print=lowercase_,trace_memory_line_by_line=lowercase_,multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            A__   				=       benchmark.run()
            _check_summary_is_not_empty(result.inference_summary		)
            _check_summary_is_not_empty(result.train_summary		)
            self.assertTrue(Path(os.path.join(lowercase_,'log.txt'		)		).exists()		)

