def   __A		(			_A					):



 """simple docstring"""


 __a       =  0
 __a       =  len(_A					)

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

 return num_inversions



def   __A		(			_A					):



 """simple docstring"""


 if len(_A					) <= 1:
  return arr, 0
 __a       =  len(_A					) // 2
 __a       =  arr[0:mid]
 __a       =  arr[mid:]

 __a       ,     __a       =  count_inversions_recursive(_A					)
 __a       ,     __a       =  count_inversions_recursive(_A					)
 __a       ,     __a       =  _count_cross_inversions(_A							,				_A					)

 __a       =  inversion_p + inversions_q + cross_inversions
 return c, num_inversions



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 __a       =  __a       =  __a       =  0
 while i < len(_A					) and j < len(_A					):
  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(_A					) - i
   r.append(q[j]					)
   j += 1
  else:
   r.append(p[i]					)
   i += 1

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

 return r, num_inversion



def   __A		(			):



 """simple docstring"""


 __a       =  [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)

 __a       =  count_inversions_bf(_A					)
 __a       ,     __a       =  count_inversions_recursive(_A					)

 assert num_inversions_bf == num_inversions_recursive == 8

 print("number of inversions = "							,				_A					)

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

 arr_a.sort()
 __a       =  count_inversions_bf(_A					)
 __a       ,     __a       =  count_inversions_recursive(_A					)

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

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

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


if __name__ == "__main__":
   main()



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""xlm-roberta-xl"""

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int=25_08_80     ,							__SCREAMING_SNAKE_CASE :      Dict=25_60     ,							__SCREAMING_SNAKE_CASE :      List[str]=36     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      int=1_02_40     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_14     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1E-05     ,							__SCREAMING_SNAKE_CASE :      str=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="absolute"     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  position_embedding_type
   __a       =  use_cache
   __a       =  classifier_dropout
class    A_ (       a_ ):

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if self.task == "multiple-choice":
    __a       =  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    __a       =  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]       )


