


'''simple docstring'''






from typing import TYPE_CHECKING

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


a_   :      List[str]				    =      {
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_   :      Dict				    =      ["AlbertTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_   :      Tuple				    =      ["AlbertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_   :      Union[str, Any]				    =      [
	    "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "AlbertForMaskedLM",
	    "AlbertForMultipleChoice",
	    "AlbertForPreTraining",
	    "AlbertForQuestionAnswering",
	    "AlbertForSequenceClassification",
	    "AlbertForTokenClassification",
	    "AlbertModel",
	    "AlbertPreTrainedModel",
	    "load_tf_weights_in_albert",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_   :      Optional[Any]				    =      [
	    "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFAlbertForMaskedLM",
	    "TFAlbertForMultipleChoice",
	    "TFAlbertForPreTraining",
	    "TFAlbertForQuestionAnswering",
	    "TFAlbertForSequenceClassification",
	    "TFAlbertForTokenClassification",
	    "TFAlbertMainLayer",
	    "TFAlbertModel",
	    "TFAlbertPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_   :      List[str]				    =      [
	    "FlaxAlbertForMaskedLM",
	    "FlaxAlbertForMultipleChoice",
	    "FlaxAlbertForPreTraining",
	    "FlaxAlbertForQuestionAnswering",
	    "FlaxAlbertForSequenceClassification",
	    "FlaxAlbertForTokenClassification",
	    "FlaxAlbertModel",
	    "FlaxAlbertPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

	a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

