


'''simple docstring'''






import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)


__UpperCAmelCase       =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCAmelCase       =							{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__UpperCAmelCase       =							{'''facebook/blenderbot_small-90M''': 512}


def  _snake_case       (					A	)						->   List[str]:
							lowerCAmelCase__	       =     set()
							lowerCAmelCase__	       =     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)	)
														lowerCAmelCase__	       =     char

							lowerCAmelCase__	       =     set(A	)
							return pairs






class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				VOCAB_FILES_NAMES
							lowercase__      :       Optional[Any]     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Tuple     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       int     						=				["input_ids", "attention_mask"]







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_="__start__" ,   lowerCamelCase_="__end__" ,   lowerCamelCase_="__unk__" ,   lowerCamelCase_="__null__" ,   **lowerCamelCase_ ,   )	->							Dict:
														super().__init__(unk_token=lowerCamelCase_ ,   bos_token=lowerCamelCase_ ,   eos_token=lowerCamelCase_ ,   pad_token=lowerCamelCase_ ,   **lowerCamelCase_       )

														with open(lowerCamelCase_ ,   encoding='''utf-8'''       ) as vocab_handle:
																					lowerCAmelCase__	       =     json.load(lowerCamelCase_       )
														lowerCAmelCase__	       =     {v: k for k, v in self.encoder.items()}
														with open(lowerCamelCase_ ,   encoding='''utf-8'''       ) as merges_handle:
																					lowerCAmelCase__	       =     merges_handle.read().split('''\n'''       )[1:-1]
														lowerCAmelCase__	       =     [tuple(merge.split()       ) for merge in merges]
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
														lowerCAmelCase__	       =     {}







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return len(self.encoder       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return dict(self.encoder ,   **self.added_tokens_encoder       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														if token in self.cache:
																					return self.cache[token]
														lowerCAmelCase__	       =     re.sub('''([.,!?()])''' ,   r''' \1''' ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     re.sub('''(\')''' ,   r''' \1 ''' ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     re.sub(r'''\s{2,}''' ,   ''' ''' ,   lowerCamelCase_       )
														if "\n" in token:
																					lowerCAmelCase__	       =     token.replace('''\n''' ,   ''' __newln__'''       )

														lowerCAmelCase__	       =     token.split(''' '''       )
														lowerCAmelCase__	       =     []
														for token in tokens:
																					if not len(lowerCamelCase_       ):
																												continue

																					lowerCAmelCase__	       =     token.lower()
																					lowerCAmelCase__	       =     tuple(lowerCamelCase_       )
																					lowerCAmelCase__	       =     tuple(list(word[:-1]       ) + [word[-1] + '''</w>''']       )
																					lowerCAmelCase__	       =     get_pairs(lowerCamelCase_       )

																					if not pairs:
																												words.append(lowerCamelCase_       )
																												continue

																					while True:
																												lowerCAmelCase__	       =     min(lowerCamelCase_ ,   key=lambda lowerCamelCase_       : self.bpe_ranks.get(lowerCamelCase_ ,   float('''inf'''       )       )       )
																												if bigram not in self.bpe_ranks:
																																			break
																												lowerCAmelCase__       , lowerCAmelCase__	       =     bigram
																												lowerCAmelCase__	       =     []
																												lowerCAmelCase__	       =     0

																												while i < len(lowerCamelCase_       ):
																																			try:
																																										lowerCAmelCase__	       =     word.index(lowerCamelCase_ ,   lowerCamelCase_       )
																																										new_word.extend(word[i:j]       )
																																										lowerCAmelCase__	       =     j
																																			except ValueError:
																																										new_word.extend(word[i:]       )
																																										break

																																			if word[i] == first and i < len(lowerCamelCase_       ) - 1 and word[i + 1] == second:
																																										new_word.append(first + second       )
																																										i += 2
																																			else:
																																										new_word.append(word[i]       )
																																										i += 1
																												lowerCAmelCase__	       =     tuple(lowerCamelCase_       )
																												lowerCAmelCase__	       =     new_word
																												if len(lowerCamelCase_       ) == 1:
																																			break
																												else:
																																			lowerCAmelCase__	       =     get_pairs(lowerCamelCase_       )
																					lowerCAmelCase__	       =     '''@@ '''.join(lowerCamelCase_       )
																					lowerCAmelCase__	       =     word[:-4]

																					lowerCAmelCase__	       =     word
																					words.append(lowerCamelCase_       )
														return " ".join(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[str]:
														lowerCAmelCase__	       =     []

														lowerCAmelCase__	       =     re.findall(r'''\S+\n?''' ,   lowerCamelCase_       )

														for token in words:
																					split_tokens.extend(list(self.bpe(lowerCamelCase_       ).split(''' '''       )       )       )
														return split_tokens







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     token.lower()
														return self.encoder.get(lowerCamelCase_ ,   self.encoder.get(self.unk_token       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														return self.decoder.get(lowerCamelCase_ ,   self.unk_token       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     ''' '''.join(lowerCamelCase_       ).replace('''@@ ''' ,   ''''''       ).strip()
														return out_string







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:
														if not os.path.isdir(lowerCamelCase_       ):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																					return
														lowerCAmelCase__	       =     os.path.join(
														    lowerCamelCase_ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     os.path.join(
														    lowerCamelCase_ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

														with open(lowerCamelCase_ ,   '''w''' ,   encoding='''utf-8'''       ) as f:
																					f.write(json.dumps(self.encoder ,   indent=2 ,   sort_keys=lowerCamelCase_ ,   ensure_ascii=lowerCamelCase_       ) + '''\n'''       )

														lowerCAmelCase__	       =     0
														with open(lowerCamelCase_ ,   '''w''' ,   encoding='''utf-8'''       ) as writer:
																					writer.write('''#version: 0.2\n'''       )
																					for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,   key=lambda lowerCamelCase_       : kv[1]       ):
																												if index != token_index:
																																			logger.warning(
																																			    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																																			    ''' Please check that the tokenizer is not corrupted!'''       )
																																			lowerCAmelCase__	       =     token_index
																												writer.write(''' '''.join(lowerCamelCase_       ) + '''\n'''       )
																												index += 1

														return vocab_file, merge_file



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     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


__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))