text: """@file test.py
  this file will run the test script"""
  
  
  import matlab.engine
  import sys
  import os
  import shutil
  import subprocess
  from six.moves import configparser
  import tensorflow as tf
  from test import test
  import warnings
  sys.path.append(os.getcwd())
  
  
  tf.app.flags.DEFINE_string('expdir', None, 'the exeriments directory that was used for training')
  tf.app.flags.DEFINE_string('recipe', None, 'The directory containing the recipe')
  tf.app.flags.DEFINE_string(
  	'task', None,
  	'If specified, the evaluation tasks field in the recipe will be ignored and only the given task will be evaluated.')
  tf.app.flags.DEFINE_string('computing', 'standard', 'the distributed computing system one of standart or condor')
  tf.app.flags.DEFINE_string('minmemory', None, 'The minimum required computing RAM in MB. (only for non-standard computing)')
  tf.app.flags.DEFINE_string('sweep_flag', 'False', 'whether the script was called from a sweep')
  tf.app.flags.DEFINE_string('allow_early_testing', 'False', 'whether testing is allowed before training has ended')
  tf.app.flags.DEFINE_string('duplicates', '1', 'How many duplicates of the same experiment were run')
  tf.app.flags.DEFINE_string('duplicates_ind_offset', '0', 'Index offset for duplicates')
  
  FLAGS = tf.app.flags.FLAGS
  
  
  
  def main(_):
  	"""main"""
  
  	if FLAGS.expdir is None:
  		raise Exception('no expdir specified. Command usage: run test --expdir=/path/to/recipe --recipe=/path/to/recipe')
  
  	if FLAGS.recipe is None:
  		raise Exception('no recipe specified. Command usage: run test --expdir=/path/to/recipe --recipe=/path/to/recipe')
  
  	if not os.path.isdir(FLAGS.recipe):
  		raise Exception('cannot find recipe %s' % FLAGS.recipe)
  
  	if FLAGS.minmemory == 'None':
  		FLAGS.minmemory = None
  
  	evaluator_cfg_file = os.path.join(FLAGS.recipe, 'test_evaluator.cfg')
  	database_cfg_file = os.path.join(FLAGS.recipe, 'database.conf')
  	reconstructor_cfg_file = os.path.join(FLAGS.recipe, 'reconstructor.cfg')
  	speaker_verification_handler_cfg_file = os.path.join(FLAGS.recipe, 'speaker_verification_handler.cfg')
  	scorer_cfg_file = os.path.join(FLAGS.recipe, 'scorer.cfg')
  	speaker_verification_scorer_cfg_file = os.path.join(FLAGS.recipe, 'speaker_verification_scorer.cfg')
  	postprocessor_cfg_file = os.path.join(FLAGS.recipe, 'postprocessor.cfg')
  	model_cfg_file = os.path.join(FLAGS.recipe, 'model.cfg')
  	losses_cfg_file = os.path.join(FLAGS.recipe, 'loss.cfg')
  	losses_cfg_available = True
  	if not os.path.isfile(losses_cfg_file):
  		warnings.warn('In following versions it will be required to provide a loss config file', Warning)
  		losses_cfg_available = False
  	exp_specific_computing_cfg_file = os.path.join(FLAGS.recipe, 'computing.cfg')
  	if os.path.isfile(exp_specific_computing_cfg_file) and FLAGS.computing != 'standard' and not FLAGS.minmemory:
  		parsed_exp_specific_computing_cfg = configparser.ConfigParser()
  		parsed_exp_specific_computing_cfg.read(exp_specific_computing_cfg_file)
  		exp_specific_computing_cfg = dict(parsed_exp_specific_computing_cfg.items('computing'))
  		FLAGS.minmemory = exp_specific_computing_cfg['mintestmemory']
  
  	# Assuming only one (typically the last one) training stage needs testing
  	parsed_evaluator_cfg = configparser.ConfigParser()
  	parsed_evaluator_cfg.read(evaluator_cfg_file)
  	training_stage = parsed_evaluator_cfg.get('evaluator', 'segment_length')
  
  	duplicates = int(FLAGS.duplicates)
  	duplicates_ind_offset = int(FLAGS.duplicates_ind_offset)
  	for dupl_ind in range(duplicates_ind_offset, duplicates+duplicates_ind_offset):
  		if duplicates > 1 or duplicates_ind_offset > 0:
  			expdir_run = FLAGS.expdir+'_dupl%i' % dupl_ind
  		else:
  			expdir_run = FLAGS.expdir
  
  		if not os.path.isdir(expdir_run):
  			raise BaseException('cannot find expdir %s' % expdir_run)
  
  		# Find a trained model to test
  		train_model_dir = os.path.join(expdir_run, training_stage, 'model')
  		trained_model_checkpoint = os.path.join(train_model_dir, 'network.ckpt')
  		if os.path.isfile(trained_model_checkpoint + '.meta'):
  			test_expdir_run = os.path.join(expdir_run, 'test')
  			test_model_dir = os.path.join(test_expdir_run, 'model')
  			test_model_checkpoint = os.path.join(test_model_dir, 'network.ckpt')
  		elif FLAGS.allow_early_testing == 'True':
  			# Find a trained model that has been validated for the requested segment length, but has not yet finished
  			train_model_dir = os.path.join(expdir_run, training_stage, 'logdir')
  			trained_model_checkpoint = os.path.join(train_model_dir, 'validated.ckpt')
  			if not os.path.isfile(trained_model_checkpoint + '.meta'):
  				raise BaseException(
  					'Early testing not allowed for %s as no model has yet been validated' %
  					os.path.join(expdir_run, training_stage))
  			# copy the model pickle object
  			if not os.path.islink(os.path.join(train_model_dir, 'model.pkl')):
  				os.symlink(
  					os.path.join(expdir_run, training_stage, 'model', 'model.pkl'),
  					os.path.join(train_model_dir, 'model.pkl'))
  			val_step = get_early_stop_val_step(trained_model_checkpoint)
  			test_expdir_run = os.path.join(expdir_run, 'test_early_%d' % val_step)
  			test_model_dir = os.path.join(test_expdir_run, 'model')
  			test_model_checkpoint = os.path.join(test_model_dir, 'validated.ckpt')
  		else:
  			exception_string = \
  				'Testing not (yet) allowed for %s as training has not (yet) been finished and early testing is set to %s.' % \
  				(os.path.join(expdir_run, training_stage), FLAGS.allow_early_testing)
  			if os.path.isfile(os.path.join(expdir_run, training_stage, 'logdir', 'validated.ckpt.meta')):
  				exception_string += \
  					'(However, early testing would be an option as a validated, but not yet finished, model has been found)'
  			raise BaseException(exception_string)
  	
  		# create the testing dir
  		if not os.path.isdir(test_expdir_run):
  			os.makedirs(test_expdir_run)
  	
  		# create a link to the model that will be used for testing.
  		if not os.path.isdir(test_model_dir):
  			os.symlink(train_model_dir, test_model_dir)
  	
  		# copy the config files
  		parsed_database_cfg = configparser.ConfigParser()
  		parsed_database_cfg.read(database_cfg_file)
  		segment_parsed_database_cfg = parsed_database_cfg
  	
  		for section in segment_parsed_database_cfg.sections():
  			if 'store_dir' in dict(segment_parsed_database_cfg.items(section)).keys():
  				segment_parsed_database_cfg.set(
  					section, 'store_dir', os.path.join(segment_parsed_database_cfg.get(section, 'store_dir'), training_stage))
  		with open(os.path.join(test_expdir_run, 'database.cfg'), 'w') as fid:
  			segment_parsed_database_cfg.write(fid)
  	
  		# shutil.copyfile(database_cfg_file,
  						# os.path.join(test_expdir_run, 'database.cfg'))
  		shutil.copyfile(evaluator_cfg_file,
  						os.path.join(test_expdir_run, 'evaluator.cfg'))
  
  		if os.path.isfile(reconstructor_cfg_file):
  			shutil.copyfile(reconstructor_cfg_file,
  						os.path.join(test_expdir_run, 'reconstructor.cfg'))
  
  			shutil.copyfile(scorer_cfg_file,
  							os.path.join(test_expdir_run, 'scorer.cfg'))
  
  		if os.path.isfile(speaker_verification_handler_cfg_file):
  			shutil.copyfile(speaker_verification_handler_cfg_file,
  						os.path.join(test_expdir_run, 'speaker_verification_handler.cfg'))
  
  			shutil.copyfile(speaker_verification_scorer_cfg_file,
  							os.path.join(test_expdir_run, 'speaker_verification_scorer.cfg'))
  	
  		try:
  			shutil.copyfile(postprocessor_cfg_file,
  							os.path.join(test_expdir_run, 'postprocessor.cfg'))
  		except:
  			pass
  		shutil.copyfile(model_cfg_file,
  						os.path.join(test_expdir_run, 'model.cfg'))
  		if losses_cfg_available:
  			shutil.copyfile(losses_cfg_file,
  							os.path.join(test_expdir_run, 'loss.cfg'))
  	
  		# Get all tasks
  		if FLAGS.task:
  			tasks = [FLAGS.task]
  		else:
  			evaluator_cfg = configparser.ConfigParser()
  			evaluator_cfg.read(os.path.join(test_expdir_run, 'evaluator.cfg'))
  			if evaluator_cfg.get('evaluator', 'evaluator') == 'multi_task':
  				tasks = evaluator_cfg.get('evaluator', 'tasks').split(' ')
  			else:
  				raise Exception('unkown type of evaluation %s' % evaluator_cfg.get('evaluator', 'evaluator'))
  		#
  		computing_cfg_file = 'config/computing/%s/%s.cfg' % (FLAGS.computing, 'non_distributed')
  	
  		if FLAGS.computing == 'condor':
  			if not FLAGS.minmemory:
  				# read the computing config file
  				parsed_computing_cfg = configparser.ConfigParser()
  				parsed_computing_cfg.read(computing_cfg_file)
  				computing_cfg = dict(parsed_computing_cfg.items('computing'))
  				FLAGS.minmemory = computing_cfg['mintestmemory']
  
  
  			if not os.path.isdir(os.path.join(test_expdir_run, 'outputs')):
  				os.makedirs(os.path.join(test_expdir_run, 'outputs'))
  	
  			# for each task, launch a test job
  			for task in tasks:
  				# print(' '.join(['condor_submit', 'expdir=%s' % test_expdir_run, 'test_model_checkpoint=%s' % test_model_checkpoint,
  				# 					 'task=%s' % task,
  				# 	'script=nabu/scripts/test.py', 'nabu/computing/condor/non_distributed_cpu.job']))
  				subprocess.call([
  					'condor_submit', 'expdir=%s' % test_expdir_run, 'test_model_checkpoint=%s' % test_model_checkpoint,
  					'task=%s' % task, 'memory=%s' % FLAGS.minmemory,
  					'script=nabu/scripts/test.py', 'nabu/computing/condor/non_distributed_cpu.job'])
  	
  		elif FLAGS.computing == 'torque':
  	
  			# computing_cfg_file = 'config/computing/torque/non_distributed.cfg'
  			# parsed_computing_cfg = configparser.ConfigParser()
  			# parsed_computing_cfg.read(computing_cfg_file)
  			# computing_cfg = dict(parsed_computing_cfg.items('computing'))
  	
  			if not os.path.isdir(os.path.join(test_expdir_run, 'outputs')):
  				os.makedirs(os.path.join(test_expdir_run, 'outputs'))
  	
  			# for each task, launch a test job
  			for task in tasks:
  				# minmemory = computing_cfg['minmemory']
  				call_str = \
  					'qsub -v expdir=%s test_model_checkpoint=%s task=%s,script=nabu/scripts/test.py ' \
  					'-e %s/test/outputs/main_%s.err -o %s/test/outputs/main_%s.out  nabu/computing/torque/%s' % \
  					(
  						test_expdir_run, test_model_checkpoint, task, test_expdir_run, task, test_expdir_run, task,
  						'non_distributed_short.pbs')
  				process = subprocess.Popen(call_str, stdout=subprocess.PIPE, shell=True)
  				proc_stdout = process.communicate()[0].strip()
  				print proc_stdout
  	
  		elif FLAGS.computing == 'standard':
  			os.environ['CUDA_VISIBLE_DEVICES'] = '2'
  			# for each task, launch a test job
  			for task in tasks:
  				test(expdir=test_expdir_run, test_model_checkpoint=test_model_checkpoint, task=task)
  	
  		else:
  			raise Exception('Unknown computing type %s' % FLAGS.computing)
  
  
  def get_early_stop_val_step(test_model_checkpoint):
  	class TmpHook(tf.train.SessionRunHook):
  		def begin(self):
  			collection = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='validate')
  			self._saver = tf.train.Saver(collection, sharded=True, name='LoaderAtBegin2')
  
  		def after_create_session(self, session, _):
  			self._saver.restore(session, test_model_checkpoint)
  
  	graph = tf.Graph()
  	with graph.as_default():
  		with tf.variable_scope('validate', reuse=False):
  			val_step = tf.get_variable(name='validated_step', shape=[], dtype=tf.int32)
  
  
  		tmphook = TmpHook()
  		with tf.train.SingularMonitoredSession(hooks=[tmphook]) as sess:
  			val_step_value = sess.run(val_step)
  
  	return val_step_value
  
  
  if __name__ == '__main__':
  	tf.app.run()
  