






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase     :							Union[str, Any]    ={
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase     :							Any    =[
     '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XLMForMultipleChoice''',
     '''XLMForQuestionAnswering''',
     '''XLMForQuestionAnsweringSimple''',
     '''XLMForSequenceClassification''',
     '''XLMForTokenClassification''',
     '''XLMModel''',
     '''XLMPreTrainedModel''',
     '''XLMWithLMHeadModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase     :							List[str]    =[
     '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFXLMForMultipleChoice''',
     '''TFXLMForQuestionAnsweringSimple''',
     '''TFXLMForSequenceClassification''',
     '''TFXLMForTokenClassification''',
     '''TFXLMMainLayer''',
     '''TFXLMModel''',
     '''TFXLMPreTrainedModel''',
     '''TFXLMWithLMHeadModel''',
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

 lowerCamelCase     :							List[str]    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[int]:
     UpperCamelCase__      :       Optional[Any]	    =  0
     UpperCamelCase__      :       Any	    =  len(__lowerCAmelCase      ) - 1

     while i < j:
          if nums[i] + nums[j] == target:
               return [i, j]
          elif nums[i] + nums[j] < target:
               UpperCamelCase__      :       Optional[int]	    =  i + 1
          else:
               UpperCamelCase__      :       Dict	    =  j - 1

     return []


if __name__ == "__main__":
 import doctest

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