

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)

snake_case_        =			logging.getLogger()

def      snake_case__      (   ):



     '''simple docstring'''





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


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	logging.StreamHandler(sys.stdout)
         logger.addHandler(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       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(a						,       'argv'						,       a):
                   lowercase__   :       Any				=	run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(a						,       0.666)





    @slow
    @require_torch_non_multi_gpu
    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	'\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(a)

         lowercase__   :       str				=	'\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(a)

         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            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
         self.run_and_check(a)




import numpy as np

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	int(np.ceil((x_end - xa) / h      )      )
     lowercase__   :       Dict				=	np.zeros((n + 1,)      )
     lowercase__   :       Dict				=	ya
     lowercase__   :       Union[str, Any]				=	xa

     for k in range(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Optional[int]				=	f(SCREAMING_SNAKE_CASE_     ,					y[k]      )
          lowercase__   :       Dict				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       Any				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       List[Any]				=	f(x + h     ,					y[k] + h * ka      )
          lowercase__   :       Tuple				=	y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
          x += h

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()

