





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_												=     logging.getLogger()





def    __UpperCamelCase      (    ) ->Dict:
					lowerCamelCase__       =       argparse.ArgumentParser()
					parser.add_argument("-f")
					lowerCamelCase__       =       parser.parse_args()
					return args.f







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       logging.StreamHandler(sys.stdout	)
										logger.addHandler(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       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	):
																				lowerCamelCase__       =       run_glue_deebert.main()
																				for value in result.values():
																									self.assertGreaterEqual(_lowerCAmelCase	,  0.666	)






					@slow
					@require_torch_non_multi_gpu
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "\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	)

										lowerCamelCase__       =       "\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	)

										lowerCamelCase__       =       "\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	)








A_												=     {str(digit): digit**5 for digit in range(1_0)}





def    __UpperCamelCase      (    a) ->int:
					return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a))





def    __UpperCamelCase      (    ) ->int:
					return sum(
					    number
					    for number in range(1000,     1000000)
					    if number == digits_fifth_powers_sum(a))


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

