"""simple docstring"""
def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->list[int]:
  if len(_SCREAMING_SNAKE_CASE  ) > 1:
    a__:	Dict     				=      len(_SCREAMING_SNAKE_CASE  ) // 2  # Finds the middle of the array
    a__:	List[str]     				=      arr[
        :middle_length
    ]  # Creates an array of the elements in the first half.
    a__:	Any     				=      arr[
        middle_length:
    ]  # Creates an array of the elements in the second half.
    a__:	Tuple     				=      len(_SCREAMING_SNAKE_CASE  )
    a__:	Optional[Any]     				=      len(_SCREAMING_SNAKE_CASE  )
    merge(_SCREAMING_SNAKE_CASE  )  # Starts sorting the left.
    merge(_SCREAMING_SNAKE_CASE  )  # Starts sorting the right
    a__:	Any     				=      0  # Left Counter
    a__:	Union[str, Any]     				=      0  # Right Counter
    a__:	int     				=      0  # Position Counter
    while (
        left_index < left_size and right_index < right_size
    ):  # Runs until the lowers size of the left and right are sorted.
      if left_array[left_index] < right_array[right_index]:
        a__:	Union[str, Any]     				=      left_array[left_index]
        left_index += 1
      else:
        a__:	str     				=      right_array[right_index]
        right_index += 1
      index += 1
    while (
        left_index < left_size
    ):  # Adds the left over elements in the left half of the array
      a__:	List[Any]     				=      left_array[left_index]
      left_index += 1
      index += 1
    while (
        right_index < right_size
    ):  # Adds the left over elements in the right half of the array
      a__:	str     				=      right_array[right_index]
      right_index += 1
      index += 1
  return arr


if __name__ == "__main__":
 import doctest

 doctest.testmod()



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 lowercase__   			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


