



"""simple docstring"""





def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				0
      UpperCAmelCase_     					=				len(lowerCAmelCase__ )

      for i in range(n - 1 ):
            for j in range(i + 1      ,     lowerCAmelCase__ ):
                  if arr[i] > arr[j]:
                        num_inversions += 1

      return num_inversions
def   a__      ( lowerCAmelCase__ ):
      if len(lowerCAmelCase__ ) <= 1:
            return arr, 0
      UpperCAmelCase_     					=				len(lowerCAmelCase__ ) // 2
      UpperCAmelCase_     					=				arr[0:mid]
      UpperCAmelCase_     					=				arr[mid:]

      UpperCAmelCase_					,       UpperCAmelCase_     					=				count_inversions_recursive(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				count_inversions_recursive(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				_count_cross_inversions(lowerCAmelCase__      ,     lowerCAmelCase__ )

      UpperCAmelCase_     					=				inversion_p + inversions_q + cross_inversions
      return c, num_inversions
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[]
      UpperCAmelCase_     					=				UpperCAmelCase_     					=				UpperCAmelCase_     					=				0
      while i < len(lowerCAmelCase__ ) and j < len(lowerCAmelCase__ ):
            if p[i] > q[j]:
                  # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
                  # These are all inversions. The claim emerges from the
                  # property that P is sorted.
                  num_inversion += len(lowerCAmelCase__ ) - i
                  r.append(q[j] )
                  j += 1
            else:
                  r.append(p[i] )
                  i += 1

      if i < len(lowerCAmelCase__ ):
            r.extend(p[i:] )
      else:
            r.extend(q[j:] )

      return r, num_inversion
def   a__      ( ):
      UpperCAmelCase_     					=				[10, 2, 1, 5, 5, 2, 11]

      # this arr has 8 inversions:
      # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

      UpperCAmelCase_     					=				count_inversions_bf(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				count_inversions_recursive(lowerCAmelCase__ )

      assert num_inversions_bf == num_inversions_recursive == 8

      print("number of inversions = "      ,     lowerCAmelCase__ )

      # testing an array with zero inversion (a sorted arr_1)

      arr_a.sort()
      UpperCAmelCase_     					=				count_inversions_bf(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				count_inversions_recursive(lowerCAmelCase__ )

      assert num_inversions_bf == num_inversions_recursive == 0
      print("number of inversions = "      ,     lowerCAmelCase__ )

      # an empty list should also have zero inversions
      UpperCAmelCase_     					=				[]
      UpperCAmelCase_     					=				count_inversions_bf(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				count_inversions_recursive(lowerCAmelCase__ )

      assert num_inversions_bf == num_inversions_recursive == 0
      print("number of inversions = "      ,     lowerCAmelCase__ )


if __name__ == "__main__":
  main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "WavLMForAudioFrameClassification",
						    "WavLMForCTC",
						    "WavLMForSequenceClassification",
						    "WavLMForXVector",
						    "WavLMModel",
						    "WavLMPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
