

import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__A			:					Union[str, Any]						=			logging.getLogger()





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   argparse.ArgumentParser()
    parser.add_argument("-f"       )
    SCREAMING_SNAKE_CASE										=   parser.parse_args()
    return args.f





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   logging.StreamHandler(sys.stdout			)
       logger.addHandler(__lowerCamelCase			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			int			):
       SCREAMING_SNAKE_CASE										=   get_gpu_count()

       if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
       else:
           args.insert(0     , "run_glue_deebert.py"			)
           with patch.object(__lowerCamelCase     , "argv"     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   run_glue_deebert.main()
               for value in result.values():
                   self.assertGreaterEqual(__lowerCamelCase     , 0.666			)





   @slow
   @require_torch_non_multi_gpu
   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
       self.run_and_check(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
       self.run_and_check(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
       self.run_and_check(__lowerCamelCase			)


import os





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   os.path.join(os.path.dirname(A__       )			,  "num.txt"       )
    with open(A__       ) as file_hand:
        return str(sum(int(A__       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
    print(solution())