



'''simple docstring'''


from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    '''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json''',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Tuple  =      "mctct"




 def __init__(   self							,  lowerCAmelCase_=8_0_6_5							,  lowerCAmelCase_=1_5_3_6							,  lowerCAmelCase_=3_6							,  lowerCAmelCase_=6_1_4_4							,  lowerCAmelCase_=4							,  lowerCAmelCase_=3_8_4							,  lowerCAmelCase_=9_2_0							,  lowerCAmelCase_=1e-5							,  lowerCAmelCase_=0.3							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=0.0_2							,  lowerCAmelCase_=0.3							,  lowerCAmelCase_=0.3							,  lowerCAmelCase_=1							,  lowerCAmelCase_=0							,  lowerCAmelCase_=2							,  lowerCAmelCase_=1							,  lowerCAmelCase_=0.3							,  lowerCAmelCase_=1							,  lowerCAmelCase_=(7,)							,  lowerCAmelCase_=(3,)							,  lowerCAmelCase_=8_0							,  lowerCAmelCase_=1							,  lowerCAmelCase_=None							,  lowerCAmelCase_="sum"							,  lowerCAmelCase_=False							,  **lowerCAmelCase_							,  ) ->      List[Any]:



      """simple docstring"""







      super().__init__(**lowerCAmelCase_							,  pad_token_id=lowerCAmelCase_							,  bos_token_id=lowerCAmelCase_							,  eos_token_id=lowerCAmelCase_)
      a_       		=vocab_size
      a_       		=hidden_size
      a_       		=num_hidden_layers
      a_       		=intermediate_size
      a_       		=num_attention_heads
      a_       		=attention_head_dim
      a_       		=max_position_embeddings
      a_       		=layer_norm_eps
      a_       		=layerdrop
      a_       		=hidden_act
      a_       		=initializer_range
      a_       		=hidden_dropout_prob
      a_       		=attention_probs_dropout_prob
      a_       		=pad_token_id
      a_       		=bos_token_id
      a_       		=eos_token_id
      a_       		=conv_glu_dim
      a_       		=conv_dropout
      a_       		=num_conv_layers
      a_       		=input_feat_per_channel
      a_       		=input_channels
      a_       		=conv_channels
      a_       		=ctc_loss_reduction
      a_       		=ctc_zero_infinity

      # prevents config testing fail with exporting to json
      a_       		=list(lowerCAmelCase_)
      a_       		=list(lowerCAmelCase_)

      if len(self.conv_kernel) != self.num_conv_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
               f"""but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, """
               f"""`config.num_conv_layers = {self.num_conv_layers}`.""")











'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[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=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     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(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


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

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

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


