



'''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	( )     -> Any:
		_a	: Optional[Any]								=				argparse.ArgumentParser()
		parser.add_argument('-f'    )
		_a	: int								=				parser.parse_args()
		return args.f

class       __magic_name__		(       __snake_case  ):







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[str]								=				logging.StreamHandler(sys.stdout						)
									logger.addHandler(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]						):
									_a	: List[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(_UpperCAmelCase      ,'argv'      ,_UpperCAmelCase						):
													_a	: Union[str, Any]								=				run_glue_deebert.main()
													for value in result.values():
															self.assertGreaterEqual(_UpperCAmelCase      ,0.6_66						)







							@slow
							@require_torch_non_multi_gpu
							def 				__lowercase     (				self   :			Any						):
									_a	: str								=				'''
            --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(_UpperCAmelCase						)

									_a	: Tuple								=				'''
            --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(_UpperCAmelCase						)

									_a	: Optional[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(_UpperCAmelCase						)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase     :							Tuple       						=     {
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							int       						=     [
          'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LukeForEntityClassification',
          'LukeForEntityPairClassification',
          'LukeForEntitySpanClassification',
          'LukeForMultipleChoice',
          'LukeForQuestionAnswering',
          'LukeForSequenceClassification',
          'LukeForTokenClassification',
          'LukeForMaskedLM',
          'LukeModel',
          'LukePreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
      from .tokenization_luke import LukeTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_luke import (
                LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
                LukeForEntityClassification,
                LukeForEntityPairClassification,
                LukeForEntitySpanClassification,
                LukeForMaskedLM,
                LukeForMultipleChoice,
                LukeForQuestionAnswering,
                LukeForSequenceClassification,
                LukeForTokenClassification,
                LukeModel,
                LukePreTrainedModel,
            )

else:
      import sys

      lowercase     :							Dict       						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




