





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)

_UpperCAmelCase				:       int	   =				logging.getLogger()


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      List[str]					 =		argparse.ArgumentParser()
    parser.add_argument('-f' )
    lowerCamelCase__    :      Tuple					 =		parser.parse_args()
    return args.f


class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Optional[int]	)			->	None:
     lowerCamelCase__    :      List[str]					 =		logging.StreamHandler(sys.stdout	)
     logger.addHandler(UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Dict	)			->	List[str]:
     lowerCamelCase__    :      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(UpperCAmelCase			,     'argv'			,     UpperCAmelCase	):
             lowerCamelCase__    :      Optional[int]					 =		run_glue_deebert.main()
             for value in result.values():
                 self.assertGreaterEqual(UpperCAmelCase			,     0.6_6_6	)







 @slow
 @require_torch_non_multi_gpu
 def      A_	(      self  :      str	)			->	Optional[int]:
     lowerCamelCase__    :      Any					 =		'\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(UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		'\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(UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		'\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(UpperCAmelCase	)









"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


