


'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						len(UpperCAmelCase_	)

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

 return num_inversions





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 if len(UpperCAmelCase_	) <= 1:
  return arr, 0
 lowerCamelCase_  				=						len(UpperCAmelCase_	) // 2
 lowerCamelCase_  				=						arr[0:mid]
 lowerCamelCase_  				=						arr[mid:]

 lowerCamelCase_				,lowerCamelCase_  				=						count_inversions_recursive(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_  				=						count_inversions_recursive(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_  				=						_count_cross_inversions(UpperCAmelCase_      ,       UpperCAmelCase_	)

 lowerCamelCase_  				=						inversion_p + inversions_q + cross_inversions
 return c, num_inversions





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						[]
 lowerCamelCase_  				=						lowerCamelCase_  				=						lowerCamelCase_  				=						0
 while i < len(UpperCAmelCase_	) and j < len(UpperCAmelCase_	):
  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(UpperCAmelCase_	) - i
   r.append(q[j]	)
   j += 1
  else:
   r.append(p[i]	)
   i += 1

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

 return r, num_inversion





def __snake_case      (   ):
 lowerCamelCase_  				=						[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)

 lowerCamelCase_  				=						count_inversions_bf(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_  				=						count_inversions_recursive(UpperCAmelCase_	)

 assert num_inversions_bf == num_inversions_recursive == 8

 print("number of inversions = "      ,       UpperCAmelCase_	)

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

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

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

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

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


if __name__ == "__main__":
       main()







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=2    ,						UpperCamelCase=512    ,						UpperCamelCase="cls"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						project_dim
   lowerCamelCase_  				=						pooler_fn
   lowerCamelCase_  				=						learn_encoder
   lowerCamelCase_  				=						use_attention_mask





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						[r"pooler", r"logit_scale"]
  _lowerCamelCase						=						[r"position_ids", r"predictions.decoder.bias"]
  _lowerCamelCase						=						"roberta"
  _lowerCamelCase						=						RobertaSeriesConfig






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )
   lowerCamelCase_  				=						XLMRobertaModel(UpperCamelCase  )
   lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
   lowerCamelCase_  				=						getattr(UpperCamelCase    ,						"has_pre_transformation"    ,						UpperCamelCase  )
   if self.has_pre_transformation:
    lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
    lowerCamelCase_  				=						nn.LayerNorm(config.hidden_size    ,						eps=config.layer_norm_eps  )
   self.post_init()







  def 						snake_case	(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.base_model(
       input_ids=UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						position_ids=UpperCamelCase    ,						head_mask=UpperCamelCase    ,						inputs_embeds=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						output_attentions=UpperCamelCase    ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,						return_dict=UpperCamelCase    ,						)

   if self.has_pre_transformation:
    lowerCamelCase_  				=						outputs["hidden_states"][-2]
    lowerCamelCase_  				=						self.pre_LN(UpperCamelCase  )
    lowerCamelCase_  				=						self.transformation_pre(UpperCamelCase  )

    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)
   else:
    lowerCamelCase_  				=						self.transformation(outputs.last_hidden_state  )
    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)



