



'''simple docstring'''



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowercase       :						Union[str, Any]						={
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						Tuple						=[
				    "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ErnieMForMultipleChoice",
				    "ErnieMForQuestionAnswering",
				    "ErnieMForSequenceClassification",
				    "ErnieMForTokenClassification",
				    "ErnieMModel",
				    "ErnieMPreTrainedModel",
				    "ErnieMForInformationExtraction",
				]


if TYPE_CHECKING:
				from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ernie_m import ErnieMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ernie_m import (
								    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ErnieMForInformationExtraction,
								    ErnieMForMultipleChoice,
								    ErnieMForQuestionAnswering,
								    ErnieMForSequenceClassification,
								    ErnieMForTokenClassification,
								    ErnieMModel,
								    ErnieMPreTrainedModel,
								)


else:
				import sys

				_lowercase       :						Tuple						=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





from typing import TYPE_CHECKING

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


snake_case__						      =			{
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPRContextEncoderTokenizerFast',
         'DPRQuestionEncoderTokenizerFast',
         'DPRReaderTokenizerFast',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPRContextEncoder',
         'DPRPretrainedContextEncoder',
         'DPRPreTrainedModel',
         'DPRPretrainedQuestionEncoder',
         'DPRPretrainedReader',
         'DPRQuestionEncoder',
         'DPRReader',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFDPRContextEncoder',
         'TFDPRPretrainedContextEncoder',
         'TFDPRPretrainedQuestionEncoder',
         'TFDPRPretrainedReader',
         'TFDPRQuestionEncoder',
         'TFDPRReader',
     ]


if TYPE_CHECKING:
     from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
     from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
     )

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_dpr_fast import (
              DPRContextEncoderTokenizerFast,
              DPRQuestionEncoderTokenizerFast,
              DPRReaderTokenizerFast,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpr import (
              DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPRContextEncoder,
              DPRPretrainedContextEncoder,
              DPRPreTrainedModel,
              DPRPretrainedQuestionEncoder,
              DPRPretrainedReader,
              DPRQuestionEncoder,
              DPRReader,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_dpr import (
              TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDPRContextEncoder,
              TFDPRPretrainedContextEncoder,
              TFDPRPretrainedQuestionEncoder,
              TFDPRPretrainedReader,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )

else:
     import sys

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