



'''simple docstring'''

import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.sql.Selectable"]	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[Features] = None	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       bool = False	,**_UpperCAmelCase					:       Dict	,):
									super().__init__(features=_UpperCAmelCase	,cache_dir=_UpperCAmelCase	,keep_in_memory=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Tuple        =							Sql(
									    cache_dir=_UpperCAmelCase	,features=_UpperCAmelCase	,sql=_UpperCAmelCase	,con=_UpperCAmelCase	,**_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							None
									_a     :      Dict        =							None
									_a     :      Dict        =							None
									_a     :      Optional[int]        =							None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase	,download_mode=_UpperCAmelCase	,verification_mode=_UpperCAmelCase	,base_path=_UpperCAmelCase	,)

									# Build dataset for splits
									_a     :      List[str]        =							self.builder.as_dataset(
									    split='train'	,verification_mode=_UpperCAmelCase	,in_memory=self.keep_in_memory		)
									return dataset

class 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dataset	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[int] = None	,**_UpperCAmelCase					:       Dict	,):
									if num_proc is not None and num_proc <= 0:
											raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

									_a     :      Dict        =							dataset
									_a     :      List[Any]        =							name
									_a     :      Tuple        =							con
									_a     :      Union[str, Any]        =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_a     :      List[Any]        =							num_proc
									_a     :      Tuple        =							to_sql_kwargs







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Tuple        =							self.to_sql_kwargs.pop('sql'	,_UpperCAmelCase		)
									_a     :      str        =							self.to_sql_kwargs.pop('con'	,_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.to_sql_kwargs.pop('index'	,_UpperCAmelCase		)

									_a     :      Any        =							self._write(index=_UpperCAmelCase	,**self.to_sql_kwargs		)
									return written







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Dict		):
									_a       ,					_a       ,					_a     :      Any        =							args
									_a     :      Tuple        =							{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
									_a     :      Dict        =							query_table(
									    table=self.dataset.data	,key=slice(_UpperCAmelCase	,offset + self.batch_size		)	,indices=self.dataset._indices	,)
									_a     :      Tuple        =							batch.to_pandas()
									_a     :      Dict        =							df.to_sql(self.name	,self.con	,index=_UpperCAmelCase	,**_UpperCAmelCase		)
									return num_rows or len(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      Union[str, Any]        =							0

									if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0	,len(self.dataset		)	,self.batch_size		)	,unit='ba'	,disable=not logging.is_progress_bar_enabled()	,desc='Creating SQL from Arrow format'	,):
													written += self._batch_sql((offset, index, to_sql_kwargs)		)
									else:
											_a       ,					_a     :      List[Any]        =							len(self.dataset		), self.batch_size
											with multiprocessing.Pool(self.num_proc		) as pool:
													for num_rows in logging.tqdm(
													    pool.imap(
													        self._batch_sql	,[(offset, index, to_sql_kwargs) for offset in range(0	,_UpperCAmelCase	,_UpperCAmelCase		)]	,)	,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size	,unit='ba'	,disable=not logging.is_progress_bar_enabled()	,desc='Creating SQL from Arrow format'	,):
															written += num_rows

									return written






"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=5	)							->		Union[str, Any]:


					"""simple docstring"""





					assert masked_input.count('''<mask>'''	) == 1
					_UpperCamelCase							 =  torch.tensor(tokenizer.encode(__snake_case,     add_special_tokens=__snake_case	)	).unsqueeze(0	)  # Batch size 1
					_UpperCamelCase							 =  model(__snake_case	)[0]  # The last hidden-state is the first element of the output tuple
					_UpperCamelCase							 =  (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
					_UpperCamelCase							 =  logits[0, masked_index, :]
					_UpperCamelCase							 =  logits.softmax(dim=0	)
					_UpperCamelCase ,  _UpperCamelCase							 =  prob.topk(k=__snake_case,     dim=0	)
					_UpperCamelCase							 =  ''' '''.join(
					    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(__snake_case	)	)]	)
					_UpperCamelCase							 =  tokenizer.mask_token
					_UpperCamelCase							 =  []
					for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
										_UpperCamelCase							 =  predicted_token_bpe.replace('''\u2581''',     ''' '''	)
										if " {0}".format(__snake_case	) in masked_input:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(''' {0}'''.format(__snake_case	),     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
										else:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(__snake_case,     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
					return topk_filled_outputs


_a														=		CamembertTokenizer.from_pretrained("""camembert-base""")
_a														=		CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_a														=		"""Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))






