


"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy


class  SCREAMING_SNAKE_CASE__							(						_UpperCamelCase							):
       """simple docstring"""


       a    :							str  ="EncodecFeatureExtractor"
       a    :							Tuple  =("T5Tokenizer", "T5TokenizerFast")

       def __init__(		self						,	snake_case__						,	snake_case__		):






         """simple docstring"""





         super().__init__(__a						,	__a		)
         lowerCAmelCase	:	Tuple								=				self.feature_extractor
         lowerCAmelCase	:	Optional[int]								=				False

       def 			lowercase__				(		self						,	snake_case__=None						,	snake_case__=None						,	snake_case__=True		):






         """simple docstring"""





         return self.tokenizer.get_decoder_prompt_ids(task=__a						,	language=__a						,	no_timestamps=__a		)

       def __call__(		self						,	*snake_case__						,	**snake_case__		):






         """simple docstring"""





         if self._in_target_context_manager:
           return self.current_processor(*__a						,	**__a		)

         lowerCAmelCase	:	Optional[Any]								=				kwargs.pop("audio"						,	__a		)
         lowerCAmelCase	:	Optional[int]								=				kwargs.pop("sampling_rate"						,	__a		)
         lowerCAmelCase	:	Optional[int]								=				kwargs.pop("text"						,	__a		)
         if len(__a		) > 0:
           lowerCAmelCase	:	Union[str, Any]								=				args[0]
           lowerCAmelCase	:	Tuple								=				args[1:]

         if audio is None and text is None:
           raise ValueError("You need to specify either an `audio` or `text` input to process."		)

         if text is not None:
           lowerCAmelCase	:	List[Any]								=				self.tokenizer(__a						,	**__a		)

         if audio is not None:
           lowerCAmelCase	:	List[str]								=				self.feature_extractor(__a						,	*__a						,	sampling_rate=__a						,	**__a		)

         if audio is None:
           return inputs

         elif text is None:
           return audio_inputs

         else:
           lowerCAmelCase	:	Optional[int]								=				audio_inputs["input_values"]
           if "padding_mask" in audio_inputs:
             lowerCAmelCase	:	Optional[Any]								=				audio_inputs["padding_mask"]
           return inputs

       def 			lowercase__				(		self						,	*snake_case__						,	**snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	Tuple								=				kwargs.pop("audio"						,	__a		)
         lowerCAmelCase	:	List[str]								=				kwargs.pop("padding_mask"						,	__a		)

         if len(__a		) > 0:
           lowerCAmelCase	:	Dict								=				args[0]
           lowerCAmelCase	:	Union[str, Any]								=				args[1:]

         if audio_values is not None:
           return self._decode_audio(__a						,	padding_mask=__a		)
         else:
           return self.tokenizer.batch_decode(*__a						,	**__a		)

       def 			lowercase__				(		self						,	*snake_case__						,	**snake_case__		):






         """simple docstring"""





         return self.tokenizer.decode(*__a						,	**__a		)







       def 			lowercase__				(		self						,	snake_case__						,	snake_case__ = None		):






         """simple docstring"""





         lowerCAmelCase	:	int								=				to_numpy(__a		)
         lowerCAmelCase	:	int								=				audio_values.shape

         if padding_mask is None:
           return list(__a		)

         lowerCAmelCase	:	int								=				to_numpy(__a		)

         # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
         # token (so that the generated audio values are **not** treated as padded tokens)
         lowerCAmelCase	:	int								=				seq_len - padding_mask.shape[-1]
         lowerCAmelCase	:	Optional[int]								=				1 - self.feature_extractor.padding_value
         lowerCAmelCase	:	int								=				np.pad(__a						,	((0, 0), (0, difference))						,	"constant"						,	constant_values=__a		)

         lowerCAmelCase	:	str								=				audio_values.tolist()
         for i in range(__a		):
           lowerCAmelCase	:	List[str]								=				np.asarray(audio_values[i]		)[
               padding_mask[i][None, :] != self.feature_extractor.padding_value
           ]
           lowerCAmelCase	:	Union[str, Any]								=				sliced_audio.reshape(__a						,	-1		)

         return audio_values












"""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))
