


"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__	:   Union[str, Any]          =							{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   str          =							[
					    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SamModel',
					    'SamPreTrainedModel',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   List[str]          =							[
					    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSamModel',
					    'TFSamPreTrainedModel',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   str          =							['SamImageProcessor']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					__magic_name__	:   Any          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__magic_name__	:   Optional[int]          =							logging.getLogger()





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




class  __snake_case      (lowerCamelCase     ):




							def      __a      (    self:						Optional[int]      ):
										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
										logger.addHandler(A_      )

										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

										with patch.object(A_    ,       """argv"""    ,       A_      ):
													__lowerCamelCase        =			run_glue_with_pabee.main()
													for value in result.values():
																self.assertGreaterEqual(A_    ,       0.75      )




