




from __future__ import annotations

import time

lowercase__							:	Tuple											=		list[tuple[int, int]]

lowercase__							:	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],
]

lowercase__							:	Tuple											=		[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	Any			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	Node | None		):






							"""simple docstring"""






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







class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	tuple[int, int]			,      __lowercase				:	tuple[int, int]		):






							"""simple docstring"""






							snake_case_						       =	Node(start[1]			,      start[0]			,      goal[1]			,      goal[0]			,      __lowercase		)
							snake_case_						       =	Node(goal[1]			,      goal[0]			,      goal[1]			,      goal[0]			,      __lowercase		)

							snake_case_						       =	[self.start]
							snake_case_						       =	False






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






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

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

								snake_case_						       =	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				:	Union[str, Any]			,      __lowercase				:	Node		):






							"""simple docstring"""






							snake_case_						       =	[]
							for action in delta:
								snake_case_						       =	parent.pos_x + action[1]
								snake_case_						       =	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				:	str			,      __lowercase				:	Node | None		):






							"""simple docstring"""






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







class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	Optional[int]			,      __lowercase				:	List[str]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	BreadthFirstSearch(__lowercase			,      __lowercase		)
							snake_case_						       =	BreadthFirstSearch(__lowercase			,      __lowercase		)
							snake_case_						       =	False






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






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

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

								snake_case_						       =	current_bwd_node
								snake_case_						       =	current_fwd_node

								snake_case_						       =	{
								    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				:	str			,      __lowercase				:	Node			,      __lowercase				:	Node		):






							"""simple docstring"""






							snake_case_						       =	self.fwd_bfs.retrace_path(__lowercase		)
							snake_case_						       =	self.bwd_bfs.retrace_path(__lowercase		)
							bwd_path.pop()
							bwd_path.reverse()
							snake_case_						       =	fwd_path + bwd_path
							return path


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

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

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

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

		lowercase__							:	List[str]											=		time.time()
		lowercase__							:	Tuple											=		BidirectionalBreadthFirstSearch(init, goal)
		lowercase__							:	int											=		bd_bfs.search()
		lowercase__							:	Dict											=		time.time() - start_bd_bfs_time

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






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''			,  '''False'''     )     ) is not True			,  reason='''Skipping test because should only be run when releasing minor transformers version'''			,  )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]     )
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							if self.framework == "pytorch":
								subprocess.run(
								    f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()			,      encoding="utf-8"			,      check=__lowercase			,      )
							assert hasattr(self			,      "env"		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	{
							    "enabled": True,
							    "processes_per_host": 8,
							}
							snake_case_						       =	{
							    "enabled": True,
							    "parameters": {
							        "microbatches": 4,
							        "placement_strategy": "spread",
							        "pipeline": "interleaved",
							        "optimize": "speed",
							        "partitions": 4,
							        "ddp": True,
							    },
							}

							snake_case_						       =	{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

							snake_case_						       =	"trainer" if self.script == "run_glue.py" else "smtrainer"
							# creates estimator
							return HuggingFace(
							    entry_point=self.script			,      source_dir=self.env.test_path			,      role=self.env.role			,      image_uri=self.env.image_uri			,      base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"			,      instance_count=__lowercase			,      instance_type=self.instance_type			,      debugger_hook_config=__lowercase			,      hyperparameters={
							        **self.env.hyperparameters,
							        "model_name_or_path": self.model_name_or_path,
							        "max_steps": 5_00,
							    }			,      metric_definitions=self.env.metric_definitions			,      distribution=__lowercase			,      py_version="py36"			,      )






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	Dict		):






							"""simple docstring"""






							TrainingJobAnalytics(__lowercase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)






						@parameterized.expand([(1,)]		)
						def 			snake_case__		(		self				:	Any			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.create_estimator(__lowercase		)

							# run training
							estimator.fit()

							# result dataframe
							snake_case_						       =	TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

							# extract kpis
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							snake_case_						       =	(
							    Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"			,      99_99_99		)
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy		)
							assert all(t <= self.results["eval_loss"] for t in eval_loss		)

							# dump tests result into json file to share in PR
							with open(f"{estimator.latest_training_job.name}.json"			,      "w"		) as outfile:
								json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}			,      __lowercase		)
