



'''simple docstring'''






from typing import TYPE_CHECKING

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


A_							:				Dict						=			{
    """configuration_dpr""": ["""DPR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPRConfig"""],
    """tokenization_dpr""": [
        """DPRContextEncoderTokenizer""",
        """DPRQuestionEncoderTokenizer""",
        """DPRReaderOutput""",
        """DPRReaderTokenizer""",
    ],
}


try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				str						=			[
       """DPRContextEncoderTokenizerFast""",
       """DPRQuestionEncoderTokenizerFast""",
       """DPRReaderTokenizerFast""",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				Union[str, Any]						=			[
       """DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPRContextEncoder""",
       """DPRPretrainedContextEncoder""",
       """DPRPreTrainedModel""",
       """DPRPretrainedQuestionEncoder""",
       """DPRPretrainedReader""",
       """DPRQuestionEncoder""",
       """DPRReader""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				List[Any]						=			[
       """TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFDPRContextEncoder""",
       """TFDPRPretrainedContextEncoder""",
       """TFDPRPretrainedQuestionEncoder""",
       """TFDPRPretrainedReader""",
       """TFDPRQuestionEncoder""",
       """TFDPRReader""",
   ]


if TYPE_CHECKING:
   from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
   from .tokenization_dpr import (
       DPRContextEncoderTokenizer,
       DPRQuestionEncoderTokenizer,
       DPRReaderOutput,
       DPRReaderTokenizer,
   )

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_dpr_fast import (
          DPRContextEncoderTokenizerFast,
          DPRQuestionEncoderTokenizerFast,
          DPRReaderTokenizerFast,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpr import (
          DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPRContextEncoder,
          DPRPretrainedContextEncoder,
          DPRPreTrainedModel,
          DPRPretrainedQuestionEncoder,
          DPRPretrainedReader,
          DPRQuestionEncoder,
          DPRReader,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_dpr import (
          TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDPRContextEncoder,
          TFDPRPretrainedContextEncoder,
          TFDPRPretrainedQuestionEncoder,
          TFDPRPretrainedReader,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )

else:
   import sys

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








'''simple docstring'''






def 					snake_case_      (  lowerCAmelCase_	)->	tuple[int, int]:





       '''simple docstring'''


       if len(lowerCAmelCase_	) == 1:
              return a[0], a[0]
       else:
              # split A into half.
              _UpperCAmelCase						:		Union[str, Any]      =				a[: len(lowerCAmelCase_	) // 2]
              _UpperCAmelCase						:		str      =				a[len(lowerCAmelCase_	) // 2 :]

              # 2 sub problems, 1/2 of original size.
              _UpperCAmelCase  ,_UpperCAmelCase						:		str      =				max_difference(lowerCAmelCase_	)
              _UpperCAmelCase  ,_UpperCAmelCase						:		List[str]      =				max_difference(lowerCAmelCase_	)

              # get min of first and max of second
              # linear time
              _UpperCAmelCase						:		Dict      =				min(lowerCAmelCase_	)
              _UpperCAmelCase						:		List[Any]      =				max(lowerCAmelCase_	)

              # 3 cases, either (small1, big1),
              # (min_first, max_second), (small2, big2)
              # constant comparisons
              if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
                     return smalla, biga
              elif biga - smalla > max_second - min_first:
                     return smalla, biga
              else:
                     return min_first, max_second


if __name__ == "__main__":
   import doctest

   doctest.testmod()



