





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=5)   ->    Union[str, Any]:
  # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
  assert masked_input.count("""<mask>""") == 1
  __snake_case:						str   =						torch.tensor(tokenizer.encode(SCREAMING_SNAKE_CASE__					,					add_special_tokens=SCREAMING_SNAKE_CASE__)).unsqueeze(0)  # Batch size 1
  __snake_case:						List[Any]   =						model(SCREAMING_SNAKE_CASE__)[0]  # The last hidden-state is the first element of the output tuple
  __snake_case:						str   =						(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  __snake_case:						Tuple   =						logits[0, masked_index, :]
  __snake_case:						Dict   =						logits.softmax(dim=0)
  __snake_case  ,   __snake_case:						Optional[Any]   =						prob.topk(k=SCREAMING_SNAKE_CASE__					,					dim=0)
  __snake_case:						int   =						""" """.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()) for i in range(len(SCREAMING_SNAKE_CASE__))])
  __snake_case:						List[str]   =						tokenizer.mask_token
  __snake_case:						Optional[int]   =						[]
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """)):
    __snake_case:						List[Any]   =						predicted_token_bpe.replace("""\u2581"""					,					""" """)
    if " {0}".format(SCREAMING_SNAKE_CASE__) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(""" {0}""".format(SCREAMING_SNAKE_CASE__)					,					SCREAMING_SNAKE_CASE__),
              values[index].item(),
              predicted_token,
          ))
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__),
              values[index].item(),
              predicted_token,
          ))
  return topk_filled_outputs


__UpperCAmelCase       :   Tuple				       =							CamembertTokenizer.from_pretrained("camembert-base")
__UpperCAmelCase       :   str				       =							CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

__UpperCAmelCase       :   Dict				       =							"Le camembert est <mask> :)"



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













import pytest


__UpperCAmelCase       :   Optional[Any]				       =							"__dummy_dataset1__"

__UpperCAmelCase       :   List[str]				       =							"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"







@pytest.fixture
def 			A__							(    )   ->    Optional[int]:
  return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def 			A__							(    )   ->    Tuple:
  return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[Any]   =						dataset_loading_script_name
  __snake_case:						Any   =						tmp_path / """datasets""" / script_name
  script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__)
  __snake_case:						int   =						script_dir / F'''{script_name}.py'''
  with open(SCREAMING_SNAKE_CASE__					,					"""w""") as f:
    f.write(SCREAMING_SNAKE_CASE__)
  return str(SCREAMING_SNAKE_CASE__)






