


"""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 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	@slow
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       AutoConfig.from_pretrained(UpperCAmelCase__     )
																self.assertIsNotNone(UpperCAmelCase__     )
																self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       FlaxAutoModel.from_pretrained(UpperCAmelCase__     )
																self.assertIsNotNone(UpperCAmelCase__     )
																self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )




	@slow
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			str:
						for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       AutoConfig.from_pretrained(UpperCAmelCase__     )
																self.assertIsNotNone(UpperCAmelCase__     )
																self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       FlaxAutoModel.from_pretrained(UpperCAmelCase__     )
																self.assertIsNotNone(UpperCAmelCase__     )
																self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )




	@slow
	def 						UpperCAmelCase_   (				self    :       str     ) ->			int:
						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											__SCREAMING_SNAKE_CASE		     =       AutoTokenizer.from_pretrained(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       FlaxBertModel.from_pretrained(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       tokenizer("Do you support jax jitted function?"    ,    return_tensors=TensorType.JAX     )

											@jax.jit
											def eval(**UpperCAmelCase__    :       List[Any]     ):
																return model(**UpperCAmelCase__     )

											eval(**UpperCAmelCase__     ).block_until_ready()




	@slow
	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[int]:
						for model_name in ["roberta-base", "roberta-large"]:
											__SCREAMING_SNAKE_CASE		     =       AutoTokenizer.from_pretrained(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       FlaxRobertaModel.from_pretrained(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       tokenizer("Do you support jax jitted function?"    ,    return_tensors=TensorType.JAX     )

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

											eval(**UpperCAmelCase__     ).block_until_ready()




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Any:
						with self.assertRaisesRegex(
						    UpperCAmelCase__    ,    "bert-base is not a local folder and is not a valid model identifier"     ):
											__SCREAMING_SNAKE_CASE		     =       FlaxAutoModel.from_pretrained("bert-base"     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Optional[int]:
						with self.assertRaisesRegex(
						    UpperCAmelCase__    ,    R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"     ):
											__SCREAMING_SNAKE_CASE		     =       FlaxAutoModel.from_pretrained(UpperCAmelCase__    ,    revision="aaaaaa"     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Any:
						with self.assertRaisesRegex(
						    UpperCAmelCase__    ,    "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"    ,    ):
											__SCREAMING_SNAKE_CASE		     =       FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						with self.assertRaisesRegex(UpperCAmelCase__    ,    "Use `from_pt=True` to load this model"     ):
											__SCREAMING_SNAKE_CASE		     =       FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"     )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       :							Optional[Any]        =  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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