

'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					str    )				->      Optional[int]:
     """simple docstring"""




     if index == r:
          for j in range(_UpperCamelCase    ):
               print(data[j]							,			end=' '    )
          print(' '    )
          return
     #  When no more elements are there to put in data[]
     if i >= n:
          return
     # current is included, put next at next location
     _SCREAMING_SNAKE_CASE							   =arr[i]
     combination_util(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			index + 1							,			_UpperCamelCase							,			i + 1    )
     # current is excluded, replace it with
     # next (Note that i+1 is passed, but
     # index is not changed)
     combination_util(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			i + 1    )
     # The main function that prints all combinations
     # of size r in arr[] of size n. This function
     # mainly uses combinationUtil()


def 	_lowerCAmelCase (						_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					str    )				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[0] * r
     # Print all combination using temporary array 'data[]'
     combination_util(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			0							,			_UpperCamelCase							,			0    )


if __name__ == "__main__":
   # Driver code to check the function above
   lowerCamelCase			:				Union[str, Any] 			=					[1_0, 2_0, 3_0, 4_0, 5_0]
   print_combination(arr, len(arr), 3)
   # This code is contributed by Ambuj sahu



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
