





import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(snake_case_   )    ,			"Tatoeba directory does not exist."   )
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@cached_property
						def UpperCAmelCase_			( self : Tuple		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Dict     =		tempfile.mkdtemp()
							return TatoebaConverter(save_dir=_A		)






						@slow
						def UpperCAmelCase_			( self : Optional[Any]		)							->      Any:







							"""simple docstring"""


							self.resolver.convert_models(['heb-eng']		)






						@slow
						def UpperCAmelCase_			( self : str		)							->      List[str]:







							"""simple docstring"""


							snake_case_  ,snake_case_						:     List[Any]     =		self.resolver.write_model_card('opus-mt-he-en'						,				dry_run=_A		)
							assert mmeta["long_pair"] == "heb-eng"








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MCTCTForCTC""",
						    """MCTCTModel""",
						    """MCTCTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

