




from __future__ import annotations





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=None  ) ->			Optional[int]:
         lowerCAmelCase__            =					data
         lowerCAmelCase__            =					None


    def __repr__(			self	:	str  ) ->			Optional[Any]:
         lowerCAmelCase__            =					[]
         lowerCAmelCase__            =					self
         while temp:
              string_rep.append(f'{temp.data}'  )
              lowerCAmelCase__            =					temp.next
         return "->".join(SCREAMING_SNAKE_CASE__  )






def 				_A  (		lowerCAmelCase_	:   list     ):
     """simple docstring"""

     if not elements_list:
          raise Exception("The Elements List is empty"     )

     lowerCAmelCase__            =					lowerCAmelCase__            =					Node(elements_list[0]     )
     for i in range(1  ,							len(lowerCAmelCase_     )     ):
          lowerCAmelCase__            =					Node(elements_list[i]     )
          lowerCAmelCase__            =					current.next
     return head



def 				_A  (		lowerCAmelCase_	:   Node     ):
     """simple docstring"""

     if head_node is not None and isinstance(lowerCAmelCase_  ,							lowerCAmelCase_     ):
          print_reverse(head_node.next     )
          print(head_node.data     )



def 				_A  (		):
     """simple docstring"""

     from doctest import testmod

     testmod()

     lowerCAmelCase__            =					make_linked_list([14, 52, 14, 12, 43]     )
     print("Linked List:"     )
     print(lowerCAmelCase_     )
     print("Elements in Reverse:"     )
     print_reverse(lowerCAmelCase_     )


if __name__ == "__main__":
     main()




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							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  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    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --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(__A    )

        __a					=  """
            --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(__A    )

        __a					=  """
            --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(__A    )



