




from typing import TYPE_CHECKING

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


snake_case__ :						Optional[int]     =			{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__ :						str     =			["LayoutLMTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__ :						List[str]     =			[
         "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMForMaskedLM",
         "LayoutLMForSequenceClassification",
         "LayoutLMForTokenClassification",
         "LayoutLMForQuestionAnswering",
         "LayoutLMModel",
         "LayoutLMPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__ :						Optional[int]     =			[
         "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFLayoutLMForMaskedLM",
         "TFLayoutLMForSequenceClassification",
         "TFLayoutLMForTokenClassification",
         "TFLayoutLMForQuestionAnswering",
         "TFLayoutLMMainLayer",
         "TFLayoutLMModel",
         "TFLayoutLMPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
     from .tokenization_layoutlm import LayoutLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlm import (
              LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMForMaskedLM,
              LayoutLMForQuestionAnswering,
              LayoutLMForSequenceClassification,
              LayoutLMForTokenClassification,
              LayoutLMModel,
              LayoutLMPreTrainedModel,
          )
     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlm import (
              TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForQuestionAnswering,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForTokenClassification,
              TFLayoutLMMainLayer,
              TFLayoutLMModel,
              TFLayoutLMPreTrainedModel,
          )

else:
     import sys

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










from typing import TYPE_CHECKING

from ...utils import _LazyModule


snake_case__ :						List[str]     =			{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
   from .tokenization_phobert import PhobertTokenizer

else:
   import sys

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




