
'''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)

UpperCAmelCase    					=   logging.getLogger()

def      A (       ):
						snake_case       :     Optional[Any] =    argparse.ArgumentParser()
						parser.add_argument('''-f'''       )
						snake_case       :     str =    parser.parse_args()
						return args.f





class 						a   (				__magic_name__    ):




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Union[str, Any] =    logging.StreamHandler(sys.stdout      )
										logger.addHandler(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     List[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(SCREAMING_SNAKE_CASE_,    '''argv''',    SCREAMING_SNAKE_CASE_      ):
																						snake_case       :     Optional[int] =    run_glue_deebert.main()
																						for value in result.values():
																												self.assertGreaterEqual(SCREAMING_SNAKE_CASE_,    0.666      )




				@slow
				@require_torch_non_multi_gpu
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Optional[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_      )

										snake_case       :     Union[str, 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
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
										self.run_and_check(SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[str] =    '''
            --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'''





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										warnings.warn(
										    '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
										    ''' Please use LayoutLMv2ImageProcessor instead.''',    SCREAMING_SNAKE_CASE_,    )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )

