






'''simple docstring'''







import re


def 						__UpperCAmelCase							(     snake_case__							):







      def docstring_decorator(snake_case__							):
            __A		   =      fn.__doc__
            __A		   =      func_doc.split("\n"							)
            __A		   =      0
            while i < len(_UpperCamelCase							) and re.search(r"^\s*Examples?:\s*$"							,			lines[i]							) is None:
                  i += 1
            if i < len(_UpperCamelCase							):
                  __A		   =      example_docstring
                  __A		   =      "\n".join(_UpperCamelCase							)
            else:
                  raise ValueError(
                      f"The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, "
                      f"current docstring is:\n{func_doc}"							)
            __A		   =      func_doc
            return fn

      return docstring_decorator










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A      :       str      =	{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A      :       str      =	[
           'UMT5EncoderModel',
           'UMT5ForConditionalGeneration',
           'UMT5ForQuestionAnswering',
           'UMT5Model',
           'UMT5PreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       __A      :       Optional[int]      =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


