


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 			_UpperCamelCase				( lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__=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		:	Optional[int]						     =						torch.tensor(tokenizer.encode(__A       ,			add_special_tokens=__A ) ).unsqueeze(0 )  # Batch size 1
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						model(__A )[0]  # The last hidden-state is the first element of the output tuple
						__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						logits[0, masked_index, :]
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						logits.softmax(dim=0 )
						__SCREAMING_SNAKE_CASE		:	Dict						     =						prob.topk(k=__A       ,			dim=0 )
						__SCREAMING_SNAKE_CASE		:	Any						     =						''' '''.join(
						    [tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(__A ) )] )
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						tokenizer.mask_token
						__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						[]
						for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
												__SCREAMING_SNAKE_CASE		:	int						     =						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


__lowerCAmelCase      :					Tuple    							=CamembertTokenizer.from_pretrained('camembert-base')
__lowerCAmelCase      :					int    							=CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

__lowerCAmelCase      :					Dict    							='Le camembert est <mask> :)'
print(fill_mask(masked_input, model, tokenizer, topk=3))








'''simple docstring'''







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCAmelCase									=   object()

# For specifying empty leaf dict `{}`
__lowerCAmelCase									=   object()





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Tuple   ):
				a_		:	List[Any]									=   tuple((re.compile(x + '''$'''   ) for x in qs)   )
				for i in range(len(__A   ) - len(__A   ) + 1   ):
								a_		:	Union[str, Any]									=   [x.match(__A   ) for x, y in zip(__A    ,		ks[i:]   )]
								if matches and all(__A   ):
												return True
				return False





def 		_UpperCAmelCase (				__A				: List[str]   ):
				def replace(__A				: int    ,		__A				: Union[str, Any]   ):
								for rule, replacement in rules:
												if _match(__A    ,		__A   ):
																return replacement
								return val

				return replace





def 		_UpperCAmelCase (				):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('''mp'''    ,		__A   )),
				    (("transformer", "wte", "embedding"), P('''mp'''    ,		__A   )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__A    ,		'''mp'''   )),
				    (("attention", "out_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__A    ,		'''mp'''   )),
				    (("mlp", "c_fc", "bias"), P('''mp'''   )),
				    (("mlp", "c_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	Tuple									=   _get_partition_rules()
				a_		:	Tuple									=   _replacement_rules(__A   )
				a_		:	Optional[Any]									=   {k: _unmatched for k in flatten_dict(__A   )}
				a_		:	Optional[Any]									=   {k: replace(__A    ,		__A   ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__A   )   )




