
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
			import torch

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm


__magic_name__									=					logging.get_logger(__name__)


@dataclass
class  _lowerCAmelCase    (    __A    ):
							lowercase_  :  Dict      =				[
							    '''no_inference''',
							    '''no_cuda''',
							    '''no_tpu''',
							    '''no_speed''',
							    '''no_memory''',
							    '''no_env_print''',
							    '''no_multi_process''',
							]



							def __init__(   self			,    **a_ )				->				Optional[Any]:
								for deprecated_arg in self.deprecated_args:
									if deprecated_arg in kwargs:
										_UpperCAmelCase			=    deprecated_arg[3:]
										setattr(self			,    __A			,    not kwargs.pop(__A ) )
										logger.warning(
										    f"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
										    f" {positive_arg}={kwargs[positive_arg]}" )

								_UpperCAmelCase			=    kwargs.pop("torchscript"			,    self.torchscript )
								_UpperCAmelCase			=    kwargs.pop("torch_xla_tpu_print_metrics"			,    self.torch_xla_tpu_print_metrics )
								_UpperCAmelCase			=    kwargs.pop("fp16_opt_level"			,    self.fpaa_opt_level )
								super().__init__(**__A )

							lowercase_  :  Optional[int]      =				field(default=__A     ,							metadata={'''help''': '''Trace the models using torchscript'''}    )
							lowercase_  :  Optional[Any]      =				field(default=__A     ,							metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}    )
							lowercase_  :  Dict      =				field(
							    default='''O1'''     ,							metadata={
							        '''help''': (
							            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
							            '''See details at https://nvidia.github.io/apex/amp.html'''
							        )
							    }     ,							)



							@cached_property
							def    _a (   self )				->				Union[str, Any]:
								requires_backends(self			,    ["torch"] )
								logger.info("PyTorch: setting up devices" )
								if not self.cuda:
									_UpperCAmelCase			=    torch.device("cpu" )
									_UpperCAmelCase			=    0
								elif is_torch_tpu_available():
									_UpperCAmelCase			=    xm.xla_device()
									_UpperCAmelCase			=    0
								else:
									_UpperCAmelCase			=    torch.device("cuda" if torch.cuda.is_available() else "cpu" )
									_UpperCAmelCase			=    torch.cuda.device_count()
								return device, n_gpu



							@property
							def    _a (   self )				->				Optional[Any]:
								return is_torch_tpu_available() and self.tpu



							@property
							def    _a (   self )				->				Tuple:
								requires_backends(self			,    ["torch"] )
								# TODO(PVP): currently only single GPU is supported
								return torch.cuda.current_device()



							@property
							def    _a (   self )				->				List[Any]:
								requires_backends(self			,    ["torch"] )
								return self._setup_devices[0]



							@property
							def    _a (   self )				->				List[str]:
								requires_backends(self			,    ["torch"] )
								return self._setup_devices[1]



							@property
							def    _a (   self )				->				Tuple:
								return self.n_gpu > 0








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							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  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    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --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(__A    )

        __a					=  """
            --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(__A    )

        __a					=  """
            --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(__A    )



