



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)

SCREAMING_SNAKE_CASE__       =      logging.getLogger()


def A						(       )						->      Any:
			A__       =    argparse.ArgumentParser()
			parser.add_argument('-f'					)
			A__       =    parser.parse_args()
			return args.f






class      __lowerCAmelCase     (		__UpperCAmelCase      ):
					"""simple docstring"""






					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    logging.StreamHandler(sys.stdout					)
								logger.addHandler(_lowerCamelCase					)



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    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					):
														A__       =    run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase				,					0.666					)



					@slow
					@require_torch_non_multi_gpu
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    '\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					)

								A__       =    '\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					)

								A__       =    '\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 A						(       __UpperCamelCase					)						->      bool:
			return number & 1 == 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




