


"""simple docstring"""
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''': 650, '''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''': 600, '''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''': 600, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]				)
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  Union[str, 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=SCREAMING_SNAKE_CASE      ,   )
      assert hasattr(self      ,   """env""")





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Any   =		F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
      # distributed data settings
      _lowerCamelCase	:					Tuple   =		{"""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=SCREAMING_SNAKE_CASE      ,   instance_count=SCREAMING_SNAKE_CASE      ,   instance_type=self.instance_type      ,   debugger_hook_config=SCREAMING_SNAKE_CASE      ,   hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,   metric_definitions=self.env.metric_definitions      ,   distribution=SCREAMING_SNAKE_CASE      ,   py_version="""py36"""      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      TrainingJobAnalytics(SCREAMING_SNAKE_CASE).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv')





   @parameterized.expand([(2,)])
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      # create estimator
      _lowerCamelCase	:					str   =		self.create_estimator(SCREAMING_SNAKE_CASE)

      # run training
      estimator.fit()

      # result dataframe
      _lowerCamelCase	:					str   =		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

      # extract kpis
      _lowerCamelCase	:					int   =		list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
      _lowerCamelCase	:					str   =		list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      _lowerCamelCase	:					str   =		(
          Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""      ,   99_9999)
      )

      # 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}      ,   SCREAMING_SNAKE_CASE)








from collections import defaultdict


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      total  # total no of tasks (N)

        # DP table will have a dimension of (2^M)*N
        # initially all values are set to -1
        A_       :					str        =      [
            [-1 for i in range(total + 1	)] for j in range(2 ** len(snake_case	)	)
        ]

        A_       :					Optional[int]        =      defaultdict(snake_case	)  # stores the list of persons for each task

        # final_mask is used to check if all persons are included by setting all bits
        # to 1
        A_       :					Dict        =      (1 << len(snake_case	)) - 1



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Union[str, Any]      ,   snake_case							:str	):





        '''simple docstring'''




        if mask == self.final_mask:
           return 1

        # if not everyone gets the task and no more tasks are available, return 0
        if task_no > self.total_tasks:
           return 0

        # if case already considered
        if self.dp[mask][task_no] != -1:
           return self.dp[mask][task_no]

        # Number of ways when we don't this task in the arrangement
        A_       :					Tuple        =      self.count_ways_until(snake_case      ,   task_no + 1	)

        # now assign the tasks one by one to all possible persons and recursively
        # assign for the remaining tasks.
        if task_no in self.task:
           for p in self.task[task_no]:
              # if p is already given a task
              if mask & (1 << p):
                 continue

              # assign this task to p and change the mask value. And recursively
              # assign tasks with the new mask value.
              total_ways_util += self.count_ways_until(mask | (1 << p)      ,   task_no + 1	)

        # save the value.
        A_       :					Any        =      total_ways_util

        return self.dp[mask][task_no]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[str]	):





        '''simple docstring'''




        for i in range(len(snake_case	)	):
           for j in task_performed[i]:
              self.task[j].append(snake_case	)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
        return self.count_ways_until(0      ,   1	)


if __name__ == "__main__":
 _lowerCAmelCase				:   Dict						       =						5  # total no of tasks (the value of N)

 # the list of tasks that can be done by M persons.
 _lowerCAmelCase				:   str						       =						[[1, 3, 4], [1, 2, 5], [3, 4]]
 print(
     AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
         task_performed
     )
 )
