





from typing import TYPE_CHECKING

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


_snake_case           =			{
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			["""ElectraTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ElectraForCausalLM""",
      """ElectraForMaskedLM""",
      """ElectraForMultipleChoice""",
      """ElectraForPreTraining""",
      """ElectraForQuestionAnswering""",
      """ElectraForSequenceClassification""",
      """ElectraForTokenClassification""",
      """ElectraModel""",
      """ElectraPreTrainedModel""",
      """load_tf_weights_in_electra""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFElectraForMaskedLM""",
      """TFElectraForMultipleChoice""",
      """TFElectraForPreTraining""",
      """TFElectraForQuestionAnswering""",
      """TFElectraForSequenceClassification""",
      """TFElectraForTokenClassification""",
      """TFElectraModel""",
      """TFElectraPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """FlaxElectraForCausalLM""",
      """FlaxElectraForMaskedLM""",
      """FlaxElectraForMultipleChoice""",
      """FlaxElectraForPreTraining""",
      """FlaxElectraForQuestionAnswering""",
      """FlaxElectraForSequenceClassification""",
      """FlaxElectraForTokenClassification""",
      """FlaxElectraModel""",
      """FlaxElectraPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
  from .tokenization_electra import ElectraTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_electra_fast import ElectraTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_electra import (
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
        ElectraPreTrainedModel,
        load_tf_weights_in_electra,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_electra import (
        TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFElectraForMaskedLM,
        TFElectraForMultipleChoice,
        TFElectraForPreTraining,
        TFElectraForQuestionAnswering,
        TFElectraForSequenceClassification,
        TFElectraForTokenClassification,
        TFElectraModel,
        TFElectraPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
        FlaxElectraPreTrainedModel,
    )

else:
  import sys

  _snake_case           =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













from __future__ import annotations


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  0
   lowercase__			       =  len(__magic_name__	) - 1

   while i < j:
      if nums[i] + nums[j] == target:
         return [i, j]
      elif nums[i] + nums[j] < target:
         lowercase__			       =  i + 1
      else:
         lowercase__			       =  j - 1

   return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")






