text: import os
  import sys
  sys.path.append("../..")
  from PyCLUE.tasks.run_classifier import my_clue_tasks, configs
  import tensorflow as tf
  
  # assign GPU devices or CPU devices
  # os.environ["CUDA_VISIBLE_DEVICES"] = "4"
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
  # tf.logging.set_verbosity(tf.logging.ERROR)
  
  flags.DEFINE_string("task_name", "", "oss buckets")
  flags.DEFINE_string("gpu_id", "4", "oss buckets")
  
  os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu_id
  
  # default configs: see PyCLUE.utils.classifier_utils.core
  # below are some necessary paramters required in running this task
  
  # task_name:
  #     Support: 
  #         chineseGLUE: bq, xnli, lcqmc, inews, thucnews, 
  #         CLUE: afqmc, cmnli, copa, csl, iflytek, tnews, wsc
  for task_name in FLAGS.task_name.split(","):
  	from PyCLUE.tasks.run_classifier import configs
  	if task_name == 'afqmc':
  		configs["task_name"] = task_name
  		# train parameters
  		configs["max_seq_length"] = 128
  		configs["train_batch_size"] = 32
  		configs["learning_rate"] = 1e-4
  		configs["warmup_proportion"] = 0.1
  		configs["num_train_epochs"] = 10.0
  	elif task_name == 'cmnli':
  		configs["task_name"] = task_name
  		# train parameters
  		configs["max_seq_length"] = 128
  		configs["train_batch_size"] = 32
  		configs["learning_rate"] = 1e-4
  		configs["warmup_proportion"] = 0.1
  		configs["num_train_epochs"] = 10.0
  	elif task_name == 'csl':
  		configs["task_name"] = task_name
  		# train parameters
  		configs["max_seq_length"] = 256
  		configs["train_batch_size"] = 32
  		configs["learning_rate"] = 1e-4
  		configs["warmup_proportion"] = 0.1
  		configs["num_train_epochs"] = 20.0
  	elif task_name == 'iflytek':
  		configs["task_name"] = task_name
  		# train parameters
  		configs["max_seq_length"] = 512
  		configs["train_batch_size"] = 32
  		configs["learning_rate"] = 1e-4
  		configs["warmup_proportion"] = 0.1
  		configs["num_train_epochs"] = 20.0
  	elif task_name == 'tnews':
  		configs["task_name"] = task_name
  		# train parameters
  		configs["max_seq_length"] = 512
  		configs["train_batch_size"] = 32
  		configs["learning_rate"] = 1e-4
  		configs["warmup_proportion"] = 0.1
  		configs["num_train_epochs"] = 20.0
  	elif task_name == 'wsc':
  		configs["task_name"] = task_name
  		# train parameters
  		configs["max_seq_length"] = 256
  		configs["train_batch_size"] = 32
  		configs["learning_rate"] = 1e-4
  		configs["warmup_proportion"] = 0.1
  		configs["num_train_epochs"] = 20.0
  
  	# pretrained_lm_name: 
  	#     If None, should assign `vocab_file`, `bert_config_file`, `init_checkpoint`.
  	#     Or you can choose the following models:
  	#         bert, bert_wwm_ext, albert_xlarge, albert_large, albert_base, albert_base_ext, 
  	#         albert_small, albert_tiny, roberta, roberta_wwm_ext, roberta_wwm_ext_large
  	configs["pretrained_lm_name"] = "bert_alternate_all_tiny_generator_1074200_share_token_embedding"
  	configs["vocab_file"] = "/data/alternate/electra_bert_tiny_gen_bert_tiny_dis_joint_gumbel_sharing_pretrained_embedding_mixed_mask_all/generator/vocab.txt"
  	configs["bert_config_file"] = "/data/alternate/electra_bert_tiny_gen_bert_tiny_dis_joint_gumbel_sharing_pretrained_embedding_mixed_mask_all/generator/bert_config_tiny_embed_sharing.json"
  	configs["init_checkpoint"] = "/data/alternate/electra_bert_tiny_gen_bert_tiny_dis_joint_gumbel_sharing_pretrained_embedding_mixed_mask_all/generator/generator.ckpt-1074200"
  	configs["verbose"] = 1
  
  	configs["do_train"] = True
  	configs["do_eval"] = True
  	configs["do_predict"] = True
  #	configs['use_layer_lr'] = True
  
  	my_clue_tasks(configs)
      
  
  