


import pytest

from datasets import inspect_metric, list_metrics, load_metric





@pytest.fixture
def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   Optional[Any]:





	"""simple docstring"""







	monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""							,					set()						)





@pytest.fixture
def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   List[Any]:





	"""simple docstring"""







	class       _UpperCamelCase    :

					"""simple docstring"""







					def __init__(						self				,   a__   )	->				int:
						A								=				metric_id






	class       _UpperCamelCase    :

					"""simple docstring"""







					lowerCAmelCase 		= [MetricMock(__snake_case				) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]

					def      _UpperCAmelCase					(						self   )	->				List[str]:
						return self._metrics
	monkeypatch.setattr("""datasets.inspect.huggingface_hub"""							,					HfhMock()						)





@pytest.mark.parametrize(
    """func, args"""							,					[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]						)
def   _lowerCAmelCase				(   UpperCamelCase__:     Any							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Any							,					UpperCamelCase__:     List[Any]						) ->   List[str]:





	"""simple docstring"""







	if "tmp_path" in args:
		A								=				tuple(arg if arg != """tmp_path""" else tmp_path for arg in args						)
	with pytest.warns(UpperCamelCase__							,					match="""https://huggingface.co/docs/evaluate"""						):
		func(*UpperCamelCase__						)







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[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=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	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(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



