



'''simple docstring'''


import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin







@require_jieba
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: List[str]  =      CpmAntTokenizer
 __magic_name__					: Dict  =      False




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      super().setUp()

      a_       		=[
          "<d>",
          "</d>",
          "<s>",
          "</s>",
          "</_>",
          "<unk>",
          "<pad>",
          "</n>",
          "我",
          "是",
          "C",
          "P",
          "M",
          "A",
          "n",
          "t",
      ]
      a_       		=os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["vocab_file"])
      with open(self.vocab_file							,  "w"							,  encoding="utf-8") as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))




 @tooslow
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      a_       		=CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b")
      a_       		="今天天气真好！"
      a_       		=["今天", "天气", "真", "好", "！"]
      a_       		=tokenizer.tokenize(lowerCAmelCase_)
      self.assertListEqual(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		="今天天气真好！"
      a_       		=[tokenizer.bos_token] + tokens

      a_       		=[6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_)							,  lowerCAmelCase_)

      a_       		=tokenizer.decode(lowerCAmelCase_)
      self.assertEqual(lowerCAmelCase_							,  lowerCAmelCase_)











'''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))


