




'''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)

_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.getLogger()


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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




class 			_snake_case		(     lowercase_							):




			def        lowerCAmelCase__    ( self							)    ->    None:

										'''simple docstring'''




										snake_case_      =				logging.StreamHandler(sys.stdout							)
										logger.addHandler(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				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__							):
																								snake_case_      =				run_glue_deebert.main()
																								for value in result.values():
																															self.assertGreaterEqual(a__    ,							0.6_6_6							)




			@slow
			@require_torch_non_multi_gpu
			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				"\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__							)

										snake_case_      =				"\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__							)

										snake_case_      =				"\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__							)










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Dict						=			[
		    "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CanineForMultipleChoice",
		    "CanineForQuestionAnswering",
		    "CanineForSequenceClassification",
		    "CanineForTokenClassification",
		    "CanineLayer",
		    "CanineModel",
		    "CaninePreTrainedModel",
		    "load_tf_weights_in_canine",
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:       int						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




