




"""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_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     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	str		):
       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=_UpperCAmelCase   ,							)
       assert hasattr(self   ,							'env'		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Optional[int]		):
       # configuration for running training on smdistributed Model Parallel
       _A        =       {
           'enabled': True,
           'processes_per_host': 8,
       }
       _A        =       {
           'enabled': True,
           'parameters': {
               'microbatches': 4,
               'placement_strategy': 'spread',
               'pipeline': 'interleaved',
               'optimize': 'speed',
               'partitions': 4,
               'ddp': True,
           },
       }

       _A        =       {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

       _A        =       '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=_UpperCAmelCase   ,							instance_type=self.instance_type   ,							debugger_hook_config=_UpperCAmelCase   ,							hyperparameters={
               **self.env.hyperparameters,
               'model_name_or_path': self.model_name_or_path,
               'max_steps': 500,
           }   ,							metric_definitions=self.env.metric_definitions   ,							distribution=_UpperCAmelCase   ,							py_version='py36'   ,							)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	Tuple		):
       TrainingJobAnalytics(_UpperCAmelCase		).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''		)



 @parameterized.expand([(1,)]		)
 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	Dict		):
       # create estimator
       _A        =       self.create_estimator(_UpperCAmelCase		)

       # run training
       estimator.fit()

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

       # extract kpis
       _A        =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']		)
       _A        =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']		)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       _A        =       (
           Session().describe_training_job(estimator.latest_training_job.name		).get('TrainingTimeInSeconds'   ,							999_999		)
       )

       # 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}   ,							_UpperCAmelCase		)





"""simple docstring"""

from __future__ import annotations

import time

import numpy as np

a         =      [8, 5, 9, 7]
a         =      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a         =      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	list[list[int]]   ,							_UpperCAmelCase	:	list[list[int]]   ,							):
       _A        =       claim_vector
       _A        =       allocated_resources_table
       _A        =       maximum_claim_table

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table		)
           for i in range(len(self.__allocated_resources_table[0]		)		)
       ]

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return np.array(self.__claim_vector		) - np.array(
           self.__processes_resource_summation()		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return [
           list(np.array(self.__maximum_claim_table[i]		) - np.array(_UpperCAmelCase		)		)
           for i, allocated_resource in enumerate(self.__allocated_resources_table		)
       ]

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return {self.__need().index(_UpperCAmelCase		): i for i in self.__need()}

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	int		):
       _A        =       self.__need()
       _A        =       self.__allocated_resources_table
       _A        =       self.__available_resources()
       _A        =       self.__need_index_manager()
       for kw, val in kwargs.items():
             if kw and val is True:
                   self.__pretty_data()
       print('_' * 50 + '\n'		)
       while need_list:
             _A        =       False
             for each_need in need_list:
                   _A        =       True
                   for index, need in enumerate(_UpperCAmelCase		):
                         if need > available_resources[index]:
                               _A        =       False
                               break
                   if execution:
                         _A        =       True
                         # get the original index of the process from ind_ctrl db
                         for original_need_index, need_clone in need_index_manager.items():
                               if each_need == need_clone:
                                     _A        =       original_need_index
                         print(F'''Process {process_number + 1} is executing.'''		)
                         # remove the process run from stack
                         need_list.remove(_UpperCAmelCase		)
                         # update available/freed resources stack
                         _A        =       np.array(_UpperCAmelCase		) + np.array(
                             alloc_resources_table[process_number]		)
                         print(
                             'Updated available resource stack for processes: '
                             + ' '.join([str(_UpperCAmelCase		) for x in available_resources]		)		)
                         break
             if safe:
                   print('The process is in a safe state.\n'		)
             else:
                   print('System in unsafe state. Aborting...\n'		)
                   break



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       print(' ' * 9 + 'Allocated Resource Table'		)
       for item in self.__allocated_resources_table:
             print(
                 F'''P{self.__allocated_resources_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(' ' * 9 + 'System Resource Table'		)
       for item in self.__maximum_claim_table:
             print(
                 F'''P{self.__maximum_claim_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(
           'Current Usage by Active Processes: '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__claim_vector		)		)
       print(
           'Initial Available Resources:       '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__available_resources()		)		)
       time.sleep(1		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

