'''simple docstring'''






from typing import TYPE_CHECKING

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


A__    :       Union[str, Any]              ={
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      pass

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Dict              =[
          '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''RoCBertForCausalLM''',
          '''RoCBertForMaskedLM''',
          '''RoCBertForMultipleChoice''',
          '''RoCBertForPreTraining''',
          '''RoCBertForQuestionAnswering''',
          '''RoCBertForSequenceClassification''',
          '''RoCBertForTokenClassification''',
          '''RoCBertLayer''',
          '''RoCBertModel''',
          '''RoCBertPreTrainedModel''',
          '''load_tf_weights_in_roc_bert''',
      ]

if TYPE_CHECKING:
      from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
      from .tokenization_roc_bert import RoCBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            raise OptionalDependencyNotAvailable()

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roc_bert import (
                ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoCBertForCausalLM,
                RoCBertForMaskedLM,
                RoCBertForMultipleChoice,
                RoCBertForPreTraining,
                RoCBertForQuestionAnswering,
                RoCBertForSequenceClassification,
                RoCBertForTokenClassification,
                RoCBertLayer,
                RoCBertModel,
                RoCBertPreTrainedModel,
                load_tf_weights_in_roc_bert,
            )


else:
      import sys

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













'''simple docstring'''






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 			UpperCAmelCase					(      unittest.TestCase       ):




      @cached_property
      def     lowercase__   (      self      :			int  )    ->				Any:
            _lowerCAmelCase												=							tempfile.mkdtemp()
            return TatoebaConverter(save_dir=__snake_case  )




      @slow
      def     lowercase__   (      self      :			Dict  )    ->				int:
            self.resolver.convert_models(["""heb-eng"""]  )




      @slow
      def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.resolver.write_model_card("""opus-mt-he-en"""      ,      dry_run=__snake_case  )
            assert mmeta["long_pair"] == "heb-eng"






