

import re







def a_		(       UpperCamelCase_   :							Union[str, Any]  )							->   Dict:







 """simple docstring"""

 def docstring_decorator(UpperCamelCase_   :							Tuple  ):
  lowerCamelCase      						=    fn.__doc__
  lowerCamelCase      						=    func_doc.split('\n'  )
  lowerCamelCase      						=    0
  while i < len(UpperCamelCase_  ) and re.search(R'^\s*Examples?:\s*$'   ,					lines[i]  ) is None:
   i += 1
  if i < len(UpperCamelCase_  ):
   lowerCamelCase      						=    example_docstring
   lowerCamelCase      						=    '\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}'''  )
  lowerCamelCase      						=    func_doc
  return fn

 return docstring_decorator







from typing import TYPE_CHECKING

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


_lowerCAmelCase :	Optional[Any]     				=    {
    'configuration_xlm_prophetnet': ['XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMProphetNetConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Optional[Any]     				=    ['XLMProphetNetTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	List[str]     				=    [
         'XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMProphetNetDecoder',
         'XLMProphetNetEncoder',
         'XLMProphetNetForCausalLM',
         'XLMProphetNetForConditionalGeneration',
         'XLMProphetNetModel',
         'XLMProphetNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_prophetnet import (
              XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMProphetNetDecoder,
              XLMProphetNetEncoder,
              XLMProphetNetForCausalLM,
              XLMProphetNetForConditionalGeneration,
              XLMProphetNetModel,
              XLMProphetNetPreTrainedModel,
          )

else:
     import sys

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




