



'''simple docstring'''


def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=(boundary[1] - boundary[0]) / steps
     a_       		=boundary[0]
     a_       		=boundary[1]
     a_       		=make_points(lowercase__       ,	lowercase__       ,	lowercase__       )
     a_       		=0.0
     y += (h / 2.0) * f(lowercase__       )
     for i in x_i:
          # print(i)
          y += h * f(lowercase__       )
     y += (h / 2.0) * f(lowercase__       )
     return y






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=a + h
     while x < (b - h):
          yield x
          a_       		=x + h






def  UpperCAmelCase_       (		lowercase__       ):  # enter your function here


     '''simple docstring'''







     a_       		=(x - 0) * (x - 0)
     return y






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=0.0  # Lower bound of integration
     a_       		=1.0  # Upper bound of integration
     a_       		=1_0.0  # define number of steps or resolution
     a_       		=[a, b]  # define boundary of integration
     a_       		=method_a(lowercase__       ,	lowercase__       )
     print(F"""y = {y}"""       )


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))


