



def 				snake_case_(_UpperCamelCase					)							->     list:



  """simple docstring"""






  _snake_case						=							len(lowerCAmelCase__					)
  for i in range(1			,	lowerCAmelCase__					):
    _snake_case						=							collection[i]
    _snake_case						=							0
    _snake_case						=							i - 1

    while low <= high:
      _snake_case						=							(low + high) // 2
      if val < collection[mid]:
        _snake_case						=							mid - 1
      else:
        _snake_case						=							mid + 1
    for j in range(lowerCAmelCase__			,	lowerCAmelCase__			,	-1					):
      _snake_case						=							collection[j - 1]
    _snake_case						=							val
  return collection


if __name__ == "__main__":
 __A      		=     input('''Enter numbers separated by a comma:\n''').strip()
 __A      		=     [int(item) for item in user_input.split(''',''')]
 print(binary_insertion_sort(unsorted))




# 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


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''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

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

