





"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Optional[Any]:
        # For consistency across different places the DisjunctiveConstraint is called,
        # dc.token_ids is a list of integers. It is also initialized only by integers.

        _UpperCamelCase   :						str    =							[[1, 2, 4], [1, 2, 3, 4]]
        _UpperCamelCase   :						str    =							DisjunctiveConstraint(__a						)
        self.assertTrue(isinstance(dc.token_ids       , __a						)						)

        with self.assertRaises(__a						):
              DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

        with self.assertRaises(__a						):
              DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        # We can't have constraints that are complete subsets of another. This leads to a preverse
        # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
        # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
        # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
        # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
        _UpperCamelCase   :						Optional[Any]    =							[[1, 2], [1, 2, 3, 4]]

        with self.assertRaises(__a						):
              DisjunctiveConstraint(__a						)  # fails here

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							[[1, 2, 3], [1, 2, 4]]

        _UpperCamelCase   :						str    =							DisjunctiveConstraint(__a						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						str    =							dc.update(1						)
        _UpperCamelCase   :						Optional[int]    =							stepped is True and completed is False and reset is False
        self.assertTrue(__a						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.current_seq == [1]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Dict    =							dc.update(2						)
        _UpperCamelCase   :						Optional[Any]    =							stepped is True and completed is False and reset is False
        self.assertTrue(__a						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.current_seq == [1, 2]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						List[Any]    =							dc.update(3						)
        _UpperCamelCase   :						List[Any]    =							stepped is True and completed is True and reset is False
        self.assertTrue(__a						)
        self.assertTrue(dc.completed						)  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 3]						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Union[str, Any]:
        _UpperCamelCase   :						Optional[int]    =							[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

        _UpperCamelCase   :						Optional[Any]    =							DisjunctiveConstraint(__a						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							dc.update(1						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.current_seq == [1]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						List[str]    =							dc.update(2						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.current_seq == [1, 2]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Dict    =							dc.update(4						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.current_seq == [1, 2, 4]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Dict    =							dc.update(5						)
        self.assertTrue(dc.completed						)  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

        dc.reset()

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							dc.update(1						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.remaining() == 3						)
        self.assertTrue(dc.current_seq == [1]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Dict    =							dc.update(2						)
        self.assertTrue(not dc.completed						)
        self.assertTrue(dc.remaining() == 2						)
        self.assertTrue(dc.current_seq == [1, 2]						)

        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Dict    =							dc.update(5						)
        self.assertTrue(dc.completed						)  # Completed!
        self.assertTrue(dc.remaining() == 0						)
        self.assertTrue(dc.current_seq == [1, 2, 5]						)







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							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


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

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