
"""simple docstring"""



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Tuple=5		)     ->					Optional[int]:
  assert masked_input.count('''<mask>'''		) == 1
  _snake_case					     =       torch.tensor(tokenizer.encode(UpperCamelCase_ ,					add_special_tokens=UpperCamelCase_		)		).unsqueeze(0		)  # Batch size 1
  _snake_case					     =       model(UpperCamelCase_		)[0]  # The last hidden-state is the first element of the output tuple
  _snake_case					     =       (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  _snake_case					     =       logits[0, masked_index, :]
  _snake_case					     =       logits.softmax(dim=0		)
  _snake_case     ,					_snake_case					     =       prob.topk(k=UpperCamelCase_ ,					dim=0		)
  _snake_case					     =       ''' '''.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()		) for i in range(len(UpperCamelCase_		)		)]		)
  _snake_case					     =       tokenizer.mask_token
  _snake_case					     =       []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''		)		):
    _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


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))





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__snake_case								=  logging.get_logger(__name__)


__snake_case								=  TypeVar("""DatasetType""", Dataset, IterableDataset)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = "first_exhausted"	,	)		->		DatasetType:
					'''simple docstring'''


					from .arrow_dataset import Dataset
					from .iterable_dataset import IterableDataset

					if not datasets:
										raise ValueError('Unable to interleave an empty list of datasets.'		)
					for i, dataset in enumerate(UpperCamelCase_		):
										if not isinstance(UpperCamelCase_	,	(Dataset, IterableDataset)		):
															if isinstance(UpperCamelCase_	,	(DatasetDict, IterableDatasetDict)		):
																				if not dataset:
																									raise ValueError(
																									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
																									    'is an empty dataset dictionary.'		)
																				raise ValueError(
																				    F'Dataset at position {i} has at least one split: {list(UpperCamelCase_		)}\n'
																				    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_		)		)}\']'		)
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_		).__name__}.'		)
										if i == 0:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= (
															    (Dataset, IterableDataset) if isinstance(UpperCamelCase_	,	UpperCamelCase_		) else (IterableDataset, Dataset)
															)
										elif not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															raise ValueError(
															    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'		)
					if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
										raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'		)
					if dataset_type is Dataset:
										return _interleave_map_style_datasets(
										    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	stopping_strategy=UpperCamelCase_		)
					else:
										return _interleave_iterable_datasets(
										    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	stopping_strategy=UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = 0	,	)		->		DatasetType:
					'''simple docstring'''


					if not dsets:
										raise ValueError('Unable to concatenate an empty list of datasets.'		)
					for i, dataset in enumerate(UpperCamelCase_		):
										if not isinstance(UpperCamelCase_	,	(Dataset, IterableDataset)		):
															if isinstance(UpperCamelCase_	,	(DatasetDict, IterableDatasetDict)		):
																				if not dataset:
																									raise ValueError(
																									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
																									    'is an empty dataset dictionary.'		)
																				raise ValueError(
																				    F'Dataset at position {i} has at least one split: {list(UpperCamelCase_		)}\n'
																				    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_		)		)}\']'		)
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_		).__name__}.'		)
										if i == 0:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= (
															    (Dataset, IterableDataset) if isinstance(UpperCamelCase_	,	UpperCamelCase_		) else (IterableDataset, Dataset)
															)
										elif not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															raise ValueError(
															    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'		)
					if dataset_type is Dataset:
										return _concatenate_map_style_datasets(UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	axis=UpperCamelCase_		)
					else:
										return _concatenate_iterable_datasets(UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	axis=UpperCamelCase_		)





