
"""simple docstring"""




import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
  import jax

  from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
  from transformers.models.bert.modeling_flax_bert import FlaxBertModel
  from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel


@require_flax
class       __magic_name__							(							unittest.TestCase			):
       @slow
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         for model_name in ["bert-base-cased", "bert-large-uncased"]:
           with self.subTest(A_							):
             _lowercase:      List[Any]   			=  AutoConfig.from_pretrained(A_							)
             self.assertIsNotNone(A_							)
             self.assertIsInstance(A_			,					A_							)

             _lowercase:      Dict   			=  FlaxAutoModel.from_pretrained(A_							)
             self.assertIsNotNone(A_							)
             self.assertIsInstance(A_			,					A_							)
       @slow
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         for model_name in ["roberta-base", "roberta-large"]:
           with self.subTest(A_							):
             _lowercase:      List[str]   			=  AutoConfig.from_pretrained(A_							)
             self.assertIsNotNone(A_							)
             self.assertIsInstance(A_			,					A_							)

             _lowercase:      Dict   			=  FlaxAutoModel.from_pretrained(A_							)
             self.assertIsNotNone(A_							)
             self.assertIsInstance(A_			,					A_							)
       @slow
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         for model_name in ["bert-base-cased", "bert-large-uncased"]:
           _lowercase:      int   			=  AutoTokenizer.from_pretrained(A_							)
           _lowercase:      str   			=  FlaxBertModel.from_pretrained(A_							)
           _lowercase:      int   			=  tokenizer('''Do you support jax jitted function?'''			,					return_tensors=TensorType.JAX							)

           @jax.jit
           def eval(**A_							):
             return model(**A_							)

           eval(**A_							).block_until_ready()
       @slow
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         for model_name in ["roberta-base", "roberta-large"]:
           _lowercase:      Dict   			=  AutoTokenizer.from_pretrained(A_							)
           _lowercase:      Union[str, Any]   			=  FlaxRobertaModel.from_pretrained(A_							)
           _lowercase:      Any   			=  tokenizer('''Do you support jax jitted function?'''			,					return_tensors=TensorType.JAX							)

           @jax.jit
           def eval(**A_							):
             return model(**A_							)

           eval(**A_							).block_until_ready()
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         with self.assertRaisesRegex(
             A_			,					'''bert-base is not a local folder and is not a valid model identifier'''							):
           _lowercase:      Optional[Any]   			=  FlaxAutoModel.from_pretrained('''bert-base'''							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         with self.assertRaisesRegex(
             A_			,					R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''							):
           _lowercase:      Any   			=  FlaxAutoModel.from_pretrained(A_			,					revision='''aaaaaa'''							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         with self.assertRaisesRegex(
             A_			,					'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''			,					):
           _lowercase:      Optional[int]   			=  FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''							)



       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         with self.assertRaisesRegex(A_			,					'''Use `from_pt=True` to load this model'''							):
           _lowercase:      Optional[Any]   			=  FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''							)


"""simple docstring"""




from typing import TYPE_CHECKING

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


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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