


"""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)

__snake_case		=   logging.getLogger()
def       A_   (   ):






     """simple docstring"""

     _a    	=				argparse.ArgumentParser()
     parser.add_argument('''-f'''       )
     _a    	=				parser.parse_args()
     return args.f


class     __lowerCamelCase  (					__lowerCAmelCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							None:
          _a    	=				logging.StreamHandler(sys.stdout							)
          logger.addHandler(_UpperCamelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _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(_UpperCamelCase    ,				'''argv'''    ,				_UpperCamelCase							):
                    _a    	=				run_glue_deebert.main()
                    for value in result.values():
                         self.assertGreaterEqual(_UpperCamelCase    ,				0.666							)





     @slow
     @require_torch_non_multi_gpu
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _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(_UpperCamelCase							)

          _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(_UpperCamelCase							)

          _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(_UpperCamelCase							)



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case		=       {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViTMAEForPreTraining''',
       '''ViTMAELayer''',
       '''ViTMAEModel''',
       '''ViTMAEPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''TFViTMAEForPreTraining''',
       '''TFViTMAEModel''',
       '''TFViTMAEPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)