import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=5   )		->						List[str]:




       '''simple docstring'''






       assert masked_input.count('<mask>'   ) == 1
       _A=	torch.tensor(tokenizer.encode(lowerCAmelCase_       ,					add_special_tokens=lowerCAmelCase_   )   ).unsqueeze(0   )  # Batch size 1
       _A=	model(lowerCAmelCase_   )[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=lowerCAmelCase_       ,					dim=0   )
       _A=	' '.join(
           [tokenizer.convert_ids_to_tokens(indices[i].item()   ) for i in range(len(lowerCAmelCase_   )   )]   )
       _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(lowerCAmelCase_   ) in masked_input:
                     topk_filled_outputs.append(
                         (
                             masked_input.replace(' {0}'.format(lowerCAmelCase_   )       ,					lowerCAmelCase_   ),
                             values[index].item(),
                             predicted_token,
                         )   )
              else:
                     topk_filled_outputs.append(
                         (
                             masked_input.replace(lowerCAmelCase_       ,					lowerCAmelCase_   ),
                             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))
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""speech_to_text_2"""
     _SCREAMING_SNAKE_CASE     :     str		=["""past_key_values"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		={"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}



     def __init__(					self				,      lowerCAmelCase__=10000				,      lowerCAmelCase__=6				,      lowerCAmelCase__=2048				,      lowerCAmelCase__=4				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=True				,      lowerCAmelCase__="relu"				,      lowerCAmelCase__=256				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=2				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=1024				,      **lowerCAmelCase__				,      ):
            _A=	vocab_size
            _A=	d_model
            _A=	decoder_ffn_dim
            _A=	decoder_layers
            _A=	decoder_attention_heads
            _A=	dropout
            _A=	attention_dropout
            _A=	activation_dropout
            _A=	activation_function
            _A=	init_std
            _A=	decoder_layerdrop
            _A=	use_cache
            _A=	decoder_layers
            _A=	scale_embedding  # scale factor will be sqrt(d_model) if True
            _A=	max_target_positions

            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      decoder_start_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )