



'''simple docstring'''


def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=0
     a_       		=len(lowercase__       )

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

     return num_inversions






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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

     a_		,						a_       		=count_inversions_recursive(lowercase__       )
     a_		,						a_       		=count_inversions_recursive(lowercase__       )
     a_		,						a_       		=_count_cross_inversions(lowercase__       ,	lowercase__       )

     a_       		=inversion_p + inversions_q + cross_inversions
     return c, num_inversions






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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

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

     return r, num_inversion






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=[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)

     a_       		=count_inversions_bf(lowercase__       )
     a_		,						a_       		=count_inversions_recursive(lowercase__       )

     assert num_inversions_bf == num_inversions_recursive == 8

     print("number of inversions = "       ,	lowercase__       )

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

     arr_a.sort()
     a_       		=count_inversions_bf(lowercase__       )
     a_		,						a_       		=count_inversions_recursive(lowercase__       )

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

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

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


if __name__ == "__main__":
  main()







'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

print(fill_mask(masked_input, model, tokenizer, topk=3))


