






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=5):
   # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
   assert masked_input.count('<mask>') == 1
   SCREAMING_SNAKE_CASE						= torch.tensor(tokenizer.encode(_UpperCAmelCase							,       add_special_tokens=_UpperCAmelCase)).unsqueeze(0)  # Batch size 1
   SCREAMING_SNAKE_CASE						= model(_UpperCAmelCase)[0]  # The last hidden-state is the first element of the output tuple
   SCREAMING_SNAKE_CASE						= (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
   SCREAMING_SNAKE_CASE						= logits[0, masked_index, :]
   SCREAMING_SNAKE_CASE						= logits.softmax(dim=0)
   SCREAMING_SNAKE_CASE						= prob.topk(k=_UpperCAmelCase							,       dim=0)
   SCREAMING_SNAKE_CASE						= ' '.join(
       [tokenizer.convert_ids_to_tokens(indices[i].item()) for i in range(len(_UpperCAmelCase))])
   SCREAMING_SNAKE_CASE						= tokenizer.mask_token
   SCREAMING_SNAKE_CASE						= []
   for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
      SCREAMING_SNAKE_CASE						= predicted_token_bpe.replace('\u2581'							,       ' ')
      if " {0}".format(_UpperCAmelCase) in masked_input:
         topk_filled_outputs.append(
             (
                 masked_input.replace(' {0}'.format(_UpperCAmelCase)							,       _UpperCAmelCase),
                 values[index].item(),
                 predicted_token,
             ))
      else:
         topk_filled_outputs.append(
             (
                 masked_input.replace(_UpperCAmelCase							,       _UpperCAmelCase),
                 values[index].item(),
                 predicted_token,
             ))
   return topk_filled_outputs


a_		:						str             =    CamembertTokenizer.from_pretrained('camembert-base')
a_		:						Any             =    CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

a_		:						Tuple             =    '''Le camembert est <mask> :)'''







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












a_		:						Tuple             =    {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
a_		:						List[Any]             =    {value: key for key, value in MORSE_CODE_DICT.items()}






def 				lowerCamelCase__				(_UpperCAmelCase):
			return " ".join(MORSE_CODE_DICT[char] for char in message.upper())






def 				lowerCamelCase__				(_UpperCAmelCase):
			return "".join(REVERSE_DICT[char] for char in message.split())






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'Morse code here!'
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= encrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= decrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)


if __name__ == "__main__":
				main()




