





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)

UpperCamelCase__      =logging.getLogger()






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							argparse.ArgumentParser()
     parser.add_argument("-f"	)
     _SCREAMING_SNAKE_CASE       :	str						    =							parser.parse_args()
     return args.f





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     None:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							logging.StreamHandler(sys.stdout							)
            logger.addHandler(__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							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       :	Union[str, Any]						    =							run_glue_deebert.main()
                      for value in result.values():
                           self.assertGreaterEqual(__lowerCamelCase    ,						0.666							)







       @slow
       @require_torch_non_multi_gpu
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							"\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       :	Dict						    =							"\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       :	int						    =							"\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							)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     if a < 0 or b < 0:
          raise ValueError("the value of both inputs must be positive"	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							str(bin(__lowerCamelCase	)	)[2:]  # remove the leading "0b"
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							str(bin(__lowerCamelCase	)	)[2:]
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							max(len(__lowerCamelCase	),       len(__lowerCamelCase	)	)
     return "0b" + "".join(
         str(int("1" in (char_a, char_b)	)	)
         for char_a, char_b in zip(a_binary.zfill(__lowerCamelCase	),       b_binary.zfill(__lowerCamelCase	)	)	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()