




from typing import TYPE_CHECKING

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


lowercase__   :    Optional[Any] 					=      {
    "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
    "tokenization_gpt2": ["GPT2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    Union[str, Any] 					=      ["GPT2TokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    Dict 					=      [
       "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "GPT2DoubleHeadsModel",
       "GPT2ForQuestionAnswering",
       "GPT2ForSequenceClassification",
       "GPT2ForTokenClassification",
       "GPT2LMHeadModel",
       "GPT2Model",
       "GPT2PreTrainedModel",
       "load_tf_weights_in_gpt2",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    List[str] 					=      [
       "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFGPT2DoubleHeadsModel",
       "TFGPT2ForSequenceClassification",
       "TFGPT2LMHeadModel",
       "TFGPT2MainLayer",
       "TFGPT2Model",
       "TFGPT2PreTrainedModel",
   ]

try:
   if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    List[Any] 					=      ["TFGPT2Tokenizer"]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    Union[str, Any] 					=      ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

if TYPE_CHECKING:
   from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
   from .tokenization_gpta import GPTaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_fast import GPTaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpta import (
          GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTaDoubleHeadsModel,
          GPTaForQuestionAnswering,
          GPTaForSequenceClassification,
          GPTaForTokenClassification,
          GPTaLMHeadModel,
          GPTaModel,
          GPTaPreTrainedModel,
          load_tf_weights_in_gpta,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gpta import (
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFGPTaDoubleHeadsModel,
          TFGPTaForSequenceClassification,
          TFGPTaLMHeadModel,
          TFGPTaMainLayer,
          TFGPTaModel,
          TFGPTaPreTrainedModel,
      )

   try:
      if not is_keras_nlp_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_tf import TFGPTaTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
   import sys

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__   :    Union[str, Any] 					=      {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    List[Any] 					=      [
       "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "VisualBertForMultipleChoice",
       "VisualBertForPreTraining",
       "VisualBertForQuestionAnswering",
       "VisualBertForRegionToPhraseAlignment",
       "VisualBertForVisualReasoning",
       "VisualBertLayer",
       "VisualBertModel",
       "VisualBertPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_visual_bert import (
          VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VisualBertForMultipleChoice,
          VisualBertForPreTraining,
          VisualBertForQuestionAnswering,
          VisualBertForRegionToPhraseAlignment,
          VisualBertForVisualReasoning,
          VisualBertLayer,
          VisualBertModel,
          VisualBertPreTrainedModel,
      )


else:
   import sys

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

