
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__     :		List[Any]        = logging.getLogger()

def 		UpperCamelCase						(   )		->						int:


  '''simple docstring'''






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



class    _snake_case (						UpperCAmelCase_				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   logging.StreamHandler(sys.stdout)
   logger.addHandler(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   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__	: Any     		=   run_glue_deebert.main()
       for value in result.values():
         self.assertGreaterEqual(SCREAMING_SNAKE_CASE_       ,	0.6_6_6)



 @slow
 @require_torch_non_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
   self.run_and_check(SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
   self.run_and_check(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
   self.run_and_check(SCREAMING_SNAKE_CASE_)








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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  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
  lowercase__	: Tuple     		=   (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
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

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

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


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






