


from typing import TYPE_CHECKING

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


__a	 =      {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""RoFormerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RoFormerForCausalLM""",
     """RoFormerForMaskedLM""",
     """RoFormerForMultipleChoice""",
     """RoFormerForQuestionAnswering""",
     """RoFormerForSequenceClassification""",
     """RoFormerForTokenClassification""",
     """RoFormerLayer""",
     """RoFormerModel""",
     """RoFormerPreTrainedModel""",
     """load_tf_weights_in_roformer""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRoFormerForCausalLM""",
     """TFRoFormerForMaskedLM""",
     """TFRoFormerForMultipleChoice""",
     """TFRoFormerForQuestionAnswering""",
     """TFRoFormerForSequenceClassification""",
     """TFRoFormerForTokenClassification""",
     """TFRoFormerLayer""",
     """TFRoFormerModel""",
     """TFRoFormerPreTrainedModel""",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """FlaxRoFormerForMaskedLM""",
     """FlaxRoFormerForMultipleChoice""",
     """FlaxRoFormerForQuestionAnswering""",
     """FlaxRoFormerForSequenceClassification""",
     """FlaxRoFormerForTokenClassification""",
     """FlaxRoFormerModel""",
     """FlaxRoFormerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   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 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   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 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  [1_0, 2, 1, 5, 5, 2, 1_1]

   # 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()



