



"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase						:		str			= {"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Optional[int]			= [
           "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MraForMaskedLM",
           "MraForMultipleChoice",
           "MraForQuestionAnswering",
           "MraForSequenceClassification",
           "MraForTokenClassification",
           "MraLayer",
           "MraModel",
           "MraPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mra import (
                  MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MraForMaskedLM,
                  MraForMultipleChoice,
                  MraForQuestionAnswering,
                  MraForSequenceClassification,
                  MraForTokenClassification,
                  MraLayer,
                  MraModel,
                  MraPreTrainedModel,
              )
else:
       import sys

       UpperCAmelCase						:		Tuple			= _LazyModule(__name__, globals()["__file__"], _import_structure)





"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						list[list[int]]:







  '''simple docstring'''


  lowercase_								= []
  if len(__lowerCAmelCase							) == 1:
    return [nums.copy()]
  for _ in range(len(__lowerCAmelCase							)							):
    lowercase_								= nums.pop(0							)
    lowercase_								= permute(__lowerCAmelCase							)
    for perm in permutations:
      perm.append(__lowerCAmelCase							)
    result.extend(__lowerCAmelCase							)
    nums.append(__lowerCAmelCase							)
  return result


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  def backtrack(__lowerCAmelCase							):
    if start == len(__lowerCAmelCase							) - 1:
      output.append(nums[:]							)
    else:
      for i in range(__lowerCAmelCase ,  len(__lowerCAmelCase							)							):
        lowercase_					,    lowercase_								= nums[i], nums[start]
        backtrack(start + 1							)
        lowercase_					,    lowercase_								= nums[i], nums[start]  # backtrack

  lowercase_								= []
  backtrack(0							)
  return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       UpperCAmelCase						:		Optional[Any]			= permutea([1, 2, 3])
       print(res)
       doctest.testmod()

