

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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_5_0, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]		)
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Optional[Any]			):
       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=__lowerCamelCase     , )
       assert hasattr(self     , "env"			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
       # distributed data settings
       SCREAMING_SNAKE_CASE										=   {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

       # 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=__lowerCamelCase     , instance_count=__lowerCamelCase     , instance_type=self.instance_type     , debugger_hook_config=__lowerCamelCase     , hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}     , metric_definitions=self.env.metric_definitions     , distribution=__lowerCamelCase     , py_version="py36"     , )





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Tuple			):
       TrainingJobAnalytics(__lowerCamelCase			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)





   @parameterized.expand([(2,)]			)
   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			int			):
       # create estimator
       SCREAMING_SNAKE_CASE										=   self.create_estimator(__lowerCamelCase			)

       # run training
       estimator.fit()

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

       # extract kpis
       SCREAMING_SNAKE_CASE										=   list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
       SCREAMING_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
       SCREAMING_SNAKE_CASE										=   (
           Session().describe_training_job(estimator.latest_training_job.name			).get("TrainingTimeInSeconds"     , 999999			)
       )

       # 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}     , __lowerCamelCase			)


from __future__ import annotations

__A			:					str						=			list[tuple[int, int]]

__A			:					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],
]

__A			:					List[str]						=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float     , __lowerCamelCase					:			Node | None     , ):
       SCREAMING_SNAKE_CASE										=   pos_x
       SCREAMING_SNAKE_CASE										=   pos_y
       SCREAMING_SNAKE_CASE										=   (pos_y, pos_x)
       SCREAMING_SNAKE_CASE										=   goal_x
       SCREAMING_SNAKE_CASE										=   goal_y
       SCREAMING_SNAKE_CASE										=   g_cost
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   self.calculate_heuristic()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   abs(self.pos_x - self.goal_x			)
       SCREAMING_SNAKE_CASE										=   abs(self.pos_y - self.goal_y			)
       return dx + dy





   def __lt__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return self.f_cost < other.f_cost





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			tuple[int, int]     , __lowerCamelCase					:			tuple[int, int]			):
       SCREAMING_SNAKE_CASE										=   Node(start[1]     , start[0]     , goal[1]     , goal[0]     , 0     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   Node(goal[1]     , goal[0]     , goal[1]     , goal[0]     , 99999     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   [self.start]
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   False





   def      _snake_case		(       self					:			Optional[Any]			):
       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(0			)

           if current_node.pos == self.target.pos:
               SCREAMING_SNAKE_CASE										=   True
               return self.retrace_path(__lowerCamelCase			)

           self.closed_nodes.append(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.get_successors(__lowerCamelCase			)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(__lowerCamelCase			)
               else:
                   # retrieve the best current path
                   SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase			)			)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(__lowerCamelCase			)
                   else:
                       self.open_nodes.append(__lowerCamelCase			)

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





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Node			):
       SCREAMING_SNAKE_CASE										=   []
       for action in delta:
           SCREAMING_SNAKE_CASE										=   parent.pos_x + action[1]
           SCREAMING_SNAKE_CASE										=   parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__lowerCamelCase			) - 1):
               continue

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

           successors.append(
               Node(
                   __lowerCamelCase     , __lowerCamelCase     , self.target.pos_y     , self.target.pos_x     , parent.g_cost + 1     , __lowerCamelCase     , )			)
       return successors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Node | None			):
       SCREAMING_SNAKE_CASE										=   node
       SCREAMING_SNAKE_CASE										=   []
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)			)
           SCREAMING_SNAKE_CASE										=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
    __A			:					Optional[Any]						=			(0, 0)
    __A			:					Optional[int]						=			(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __A			:					List[str]						=			GreedyBestFirst(init, goal)
    __A			:					Tuple						=			greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __A			:					Optional[Any]						=			2

        for elem in grid:
            print(elem)