




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Optional[int]  ,			A__       :					Any  ,			A__       :					Any=5	):





       '''simple docstring'''



       assert masked_input.count("""<mask>"""	) == 1
       __lowerCamelCase   =						torch.tensor(tokenizer.encode(A__  ,			add_special_tokens=A__	)	).unsqueeze(0	)  # Batch size 1
       __lowerCamelCase   =						model(A__	)[0]  # The last hidden-state is the first element of the output tuple
       __lowerCamelCase   =						(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
       __lowerCamelCase   =						logits[0, masked_index, :]
       __lowerCamelCase   =						logits.softmax(dim=0	)
       __lowerCamelCase,   __lowerCamelCase   =						prob.topk(k=A__  ,			dim=0	)
       __lowerCamelCase   =						""" """.join(
           [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A__	)	)]	)
       __lowerCamelCase   =						tokenizer.mask_token
       __lowerCamelCase   =						[]
       for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """	)	):
              __lowerCamelCase   =						predicted_token_bpe.replace("""\u2581"""  ,			""" """	)
              if " {0}".format(A__	) in masked_input:
                     topk_filled_outputs.append(
                         (
                             masked_input.replace(""" {0}""".format(A__	)  ,			A__	),
                             values[index].item(),
                             predicted_token,
                         )	)
              else:
                     topk_filled_outputs.append(
                         (
                             masked_input.replace(A__  ,			A__	),
                             values[index].item(),
                             predicted_token,
                         )	)
       return topk_filled_outputs


UpperCAmelCase_									=	CamembertTokenizer.from_pretrained('camembert-base')
UpperCAmelCase_									=	CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

UpperCAmelCase_									=	'Le camembert est <mask> :)'




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







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

