import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=5							):







				"""simple docstring"""



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


__a  :     List[str]       =  CamembertTokenizer.from_pretrained("""camembert-base""")
__a  :     List[str]       =  CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__a  :     List[str]       =  """Le camembert est <mask> :)"""




print(fill_mask(masked_input, model, tokenizer, topk=3))
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """CpmAntForCausalLM""",
		    """CpmAntModel""",
		    """CpmAntPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
		from .tokenization_cpmant import CpmAntTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cpmant import (
				    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntPreTrainedModel,
				)


else:
		import sys

		__a  :     Any       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)