
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)

_lowerCamelCase :    Optional[Any]  							= logging.getLogger()




def _a (  )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						argparse.ArgumentParser()
							parser.add_argument("-f"       )
							SCREAMING_SNAKE_CASE__       :      str     =						parser.parse_args()
							return args.f







class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							def  A_     ( self     :					str						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						logging.StreamHandler(sys.stdout						)
														logger.addHandler(_UpperCAmelCase						)






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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(_UpperCAmelCase,			"argv",			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						run_glue_deebert.main()
																												for value in result.values():
																																			self.assertGreaterEqual(_UpperCAmelCase,			0.666						)






							@slow
							@require_torch_non_multi_gpu
							def  A_     ( self     :					Dict						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						"\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(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[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            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
														self.run_and_check(_UpperCAmelCase						)

														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(_UpperCAmelCase						)













def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    int:

							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
														raise ValueError("String lengths must match!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						0

							for chara, chara in zip(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														if chara != chara:
																					count += 1

							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()






