






# 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


__lowerCamelCase	:		Dict										=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase	:		str										=   [
           """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

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













def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) <= 1:
  return [tuple(_a	)]

 snake_case__  :    Optional[int]							       =[]

 def generate(_a  :					int		,  _a  :					list	):
  if k == 1:
   res.append(tuple(arr[:]	)	)
   return

  generate(k - 1		,  _a	)

  for i in range(k - 1	):
   if k % 2 == 0:  # k is even
    snake_case__       ,   snake_case__  :    Dict							       =arr[k - 1], arr[i]
   else:  # k is odd
    snake_case__       ,   snake_case__  :    int							       =arr[k - 1], arr[0]
   generate(k - 1		,  _a	)

 generate(len(_a	)		,  _a	)
 return res


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma:\n""").strip()
       __lowerCamelCase	:		Any										=   [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))





