



'''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__ :			Tuple												=				logging.getLogger()


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	argparse.ArgumentParser()
		parser.add_argument("""-f"""      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	parser.parse_args()
		return args.f






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	logging.StreamHandler(sys.stdout		)
					logger.addHandler(lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	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(lowerCAmelCase__							,  """argv"""							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	run_glue_deebert.main()
									for value in result.values():
											self.assertGreaterEqual(lowerCAmelCase__							,  0.6_66		)



			@slow
			@require_torch_non_multi_gpu
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""
            --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(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	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
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
					self.run_and_check(lowerCAmelCase__		)

					__SCREAMING_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(lowerCAmelCase__		)




'''simple docstring'''




UpperCamelCase__ :			List[str]												=				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich