




'''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 ):





       '''simple docstring'''






       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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

                         SCREAMING_SNAKE_CASE   :      Tuple    					=						FlaxAutoModel.from_pretrained(A  )
                         self.assertIsNotNone(A  )
                         self.assertIsInstance(A,							A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(A  ):
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoConfig.from_pretrained(A  )
                         self.assertIsNotNone(A  )
                         self.assertIsInstance(A,							A  )

                         SCREAMING_SNAKE_CASE   :      str    					=						FlaxAutoModel.from_pretrained(A  )
                         self.assertIsNotNone(A  )
                         self.assertIsInstance(A,							A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoTokenizer.from_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						FlaxBertModel.from_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in ["roberta-base", "roberta-large"]:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						AutoTokenizer.from_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						FlaxRobertaModel.from_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      Optional[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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             with self.assertRaisesRegex(
                 A,							'bert-base is not a local folder and is not a valid model identifier'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						FlaxAutoModel.from_pretrained('bert-base'  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             with self.assertRaisesRegex(
                 A,							r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						FlaxAutoModel.from_pretrained(A,							revision='aaaaaa'  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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




"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





