




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      (						):
					__lowerCAmelCase				   =       argparse.ArgumentParser()
					parser.add_argument("-f"     )
					__lowerCAmelCase				   =       parser.parse_args()
					return args.f


class 			a__    (					snake_case__      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       logging.StreamHandler(sys.stdout			)
											logger.addHandler(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       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			):
																					__lowerCAmelCase				   =       run_glue_deebert.main()
																					for value in result.values():
																										self.assertGreaterEqual(_A		,			0.6_66			)






						@slow
						@require_torch_non_multi_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "\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(_A			)

											__lowerCAmelCase				   =       "\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(_A			)

											__lowerCAmelCase				   =       "\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(_A			)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    			=			{"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




