"""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,
)


_UpperCamelCase     :		Tuple											=					{
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Optional[Any]											=					["AlbertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		List[Any]											=					["AlbertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		List[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:
       _UpperCamelCase     :		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:
       _UpperCamelCase     :		Dict											=					[
           "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

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


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

