




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:     Dict									= logging.getLogger()


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




class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							logging.StreamHandler(sys.stdout  )
								logger.addHandler(snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							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(snake_case__					,							'argv'					,							snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							run_glue_deebert.main()
												for value in result.values():
														self.assertGreaterEqual(snake_case__					,							0.6_66  )






						@slow
						@require_torch_non_multi_gpu
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'\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(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'\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(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'\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(snake_case__  )





SCREAMING_SNAKE_CASE:     dict[tuple[int, int, int], int]									= {}


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# if we are absent twice, or late 3 consecutive days,
		# no further prize strings are possible
		if late == 3 or absent == 2:
				return 0

		# if we have no days left, and have not failed any other rules,
		# we have a prize string
		if days == 0:
				return 1

		# No easy solution, so now we need to do the recursive calculation

		# First, check if the combination is already in the cache, and
		# if yes, return the stored value from there since we already
		# know the number of possible prize strings from this point on
		SCREAMING_SNAKE_CASE_					      =							(days, absent, late)
		if key in cache:
				return cache[key]

		# now we calculate the three possible ways that can unfold from
		# this point on, depending on our attendance today

		# 1) if we are late (but not absent), the "absent" counter stays as
		# it is, but the "late" counter increases by one
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						late + 1 )

		# 2) if we are absent, the "absent" counter increases by 1, and the
		# "late" counter resets to 0
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						absent + 1    ,						0 )

		# 3) if we are on time, this resets the "late" counter and keeps the
		# absent counter
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						0 )

		SCREAMING_SNAKE_CASE_					      =							state_late + state_absent + state_ontime

		SCREAMING_SNAKE_CASE_					      =							prizestrings
		return prizestrings


def 					_a							(					lowerCAmelCase = 30 )->	int:
		return _calculate(lowerCAmelCase    ,						absent=0    ,						late=0 )


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