



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__      =  logging.getLogger()


def  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					argparse.ArgumentParser()
				parser.add_argument("-f"						)
				lowercase__					:   Any													=					parser.parse_args()
				return args.f

class 				snake_case__(__snake_case      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Any					):
									lowercase__					:   str													=					logging.StreamHandler(sys.stdout					)
									logger.addHandler(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   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(SCREAMING_SNAKE_CASE				,       "argv"				,       SCREAMING_SNAKE_CASE					):
																	lowercase__					:   List[Any]													=					run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(SCREAMING_SNAKE_CASE				,       0.666					)







					@slow
					@require_torch_non_multi_gpu
					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Union[str, Any]													=					"\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(SCREAMING_SNAKE_CASE					)

									lowercase__					:   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(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					"\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(SCREAMING_SNAKE_CASE					)









def  __lowerCamelCase (       lowerCamelCase__ = 1_000						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   int													=					1, 1
				lowercase__					:   List[Any]													=					[]
				for i in range(1					,			n + 1						):
								lowercase__					:   Dict													=					prev_numerator + 2 * prev_denominator
								lowercase__					:   Tuple													=					prev_numerator + prev_denominator
								if len(str(lowerCamelCase__						)						) > len(str(lowerCamelCase__						)						):
												result.append(lowerCamelCase__						)
								lowercase__					:   int													=					numerator
								lowercase__					:   int													=					denominator

				return len(lowerCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')




