




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)

_lowerCamelCase	:		str							       =   logging.getLogger()







def SCREAMING_SNAKE_CASE				(       )   ->       Union[str, Any]:



       """simple docstring"""

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


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->None:





           '''simple docstring'''

           A__       =    logging.StreamHandler(sys.stdout)
           logger.addHandler(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, UpperCAmelCase__      :	str)						->Tuple:





           '''simple docstring'''

           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        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->str:





           '''simple docstring'''

           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__)








from typing import TYPE_CHECKING

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


_lowerCamelCase	:		int							       =   {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Tuple							       =   ["""BlipImageProcessor"""]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BlipModel""",
      """BlipPreTrainedModel""",
      """BlipForConditionalGeneration""",
      """BlipForQuestionAnswering""",
      """BlipVisionModel""",
      """BlipTextModel""",
      """BlipForImageTextRetrieval""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Optional[Any]							       =   [
      """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBlipModel""",
      """TFBlipPreTrainedModel""",
      """TFBlipForConditionalGeneration""",
      """TFBlipForQuestionAnswering""",
      """TFBlipVisionModel""",
      """TFBlipTextModel""",
      """TFBlipForImageTextRetrieval""",
  ]

if TYPE_CHECKING:
  from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
  from .processing_blip import BlipProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_blip import BlipImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip import (
        BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipForConditionalGeneration,
        BlipForImageTextRetrieval,
        BlipForQuestionAnswering,
        BlipModel,
        BlipPreTrainedModel,
        BlipTextModel,
        BlipVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blip import (
        TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipPreTrainedModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )

else:
  import sys

  _lowerCamelCase	:		List[Any]							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


