


from __future__ import annotations






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: Any   =    [None] * (len(SCREAMING_SNAKE_CASE_     ) + len(SCREAMING_SNAKE_CASE_     ))

  lowerCamelCase			: List[str]   =    0  # pointer to current index for left Half
  lowerCamelCase			: int   =    0  # pointer to current index for the right Half
  lowerCamelCase			: str   =    0  # pointer to current index for the sorted array Half

  while pointera < len(SCREAMING_SNAKE_CASE_     ) and pointera < len(SCREAMING_SNAKE_CASE_     ):
    if left_half[pointera] < right_half[pointera]:
      lowerCamelCase			: str   =    left_half[pointera]
      pointera += 1
      index += 1
    else:
      lowerCamelCase			: List[Any]   =    right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(SCREAMING_SNAKE_CASE_     ):
    lowerCamelCase			: int   =    left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(SCREAMING_SNAKE_CASE_     ):
    lowerCamelCase			: Optional[Any]   =    right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if len(SCREAMING_SNAKE_CASE_     ) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  lowerCamelCase			: Optional[int]   =    0 + (len(SCREAMING_SNAKE_CASE_     ) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  lowerCamelCase			: Any   =    array[:middle]
  lowerCamelCase			: List[Any]   =    array[middle:]

  return merge(merge_sort(SCREAMING_SNAKE_CASE_     )				,						merge_sort(SCREAMING_SNAKE_CASE_     )     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case					=					{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''HubertForCTC''',
       '''HubertForSequenceClassification''',
       '''HubertModel''',
       '''HubertPreTrainedModel''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFHubertForCTC''',
       '''TFHubertModel''',
       '''TFHubertPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

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



