
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A     :  List[str]					     =       {
    "configuration_table_transformer": [
        "TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TableTransformerConfig",
        "TableTransformerOnnxConfig",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Dict					     =       [
          "TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TableTransformerForObjectDetection",
          "TableTransformerModel",
          "TableTransformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          TableTransformerConfig,
          TableTransformerOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_table_transformer import (
                TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TableTransformerForObjectDetection,
                TableTransformerModel,
                TableTransformerPreTrainedModel,
            )

else:
      import sys

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



from typing import TYPE_CHECKING

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


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

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


