





"""simple docstring"""
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   (							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :int						=  argparse.ArgumentParser()
      parser.add_argument('''-f'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()
      return args.f







class 					_SCREAMING_SNAKE_CASE(   A  ):




       def     _UpperCamelCase     (						self							)			->      None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  logging.StreamHandler(sys.stdout							)
             logger.addHandler(SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :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__							):
                         __SCREAMING_SNAKE_CASE   :Optional[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     _UpperCamelCase     (						self							)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  '''
            --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__							)

             __SCREAMING_SNAKE_CASE   :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
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
             self.run_and_check(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Any						=  '''
            --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__							)






"""simple docstring"""
from collections.abc import Callable

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()