"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_:					str     =					{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_:					str     =					["RoFormerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_:					int     =					[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_:					Tuple     =					[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_:					int     =					[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_:					Dict     =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""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    a__    (   unittest.TestCase	):


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

                  lowercase__				 =							FlaxAutoModel.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in ["roberta-base", "roberta-large"]:
             with self.subTest(_UpperCAmelCase  ):
                  lowercase__				 =							AutoConfig.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

                  lowercase__				 =							FlaxAutoModel.from_pretrained(_UpperCAmelCase  )
                  self.assertIsNotNone(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

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

             eval(**_UpperCAmelCase  ).block_until_ready()


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in ["roberta-base", "roberta-large"]:
             lowercase__				 =							AutoTokenizer.from_pretrained(_UpperCAmelCase  )
             lowercase__				 =							FlaxRobertaModel.from_pretrained(_UpperCAmelCase  )
             lowercase__				 =							tokenizer("Do you support jax jitted function?",				return_tensors=TensorType.JAX  )

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

             eval(**_UpperCAmelCase  ).block_until_ready()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(
            _UpperCAmelCase,				"bert-base is not a local folder and is not a valid model identifier"  ):
             lowercase__				 =							FlaxAutoModel.from_pretrained("bert-base"  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(
            _UpperCAmelCase,				R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"  ):
             lowercase__				 =							FlaxAutoModel.from_pretrained(_UpperCAmelCase,				revision="aaaaaa"  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(
            _UpperCAmelCase,				"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack",				):
             lowercase__				 =							FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        with self.assertRaisesRegex(_UpperCAmelCase,				"Use `from_pt=True` to load this model"  ):
             lowercase__				 =							FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"  )




