


from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{}
__UpperCAmelCase       =							{}
__UpperCAmelCase       =							{}


def  _snake_case       (					A			,   A			,   A = None			,   )						->   str:
							lowerCAmelCase__	       =     aliases if aliases is not None else []
							if format_type in _FORMAT_TYPES:
														logger.warning(
														    F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""	)
							lowerCAmelCase__	       =     formatter_cls
							for alias in set(aliases + [format_type]	):
														if alias in _FORMAT_TYPES_ALIASES:
																					logger.warning(
																					    F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""	)
														lowerCAmelCase__	       =     format_type


def  _snake_case       (					A			,   A			,   A = None	)						->   List[Any]:
							lowerCAmelCase__	       =     aliases if aliases is not None else []
							for alias in set(aliases + [format_type]	):
														lowerCAmelCase__	       =     unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
		from .torch_formatter import TorchFormatter

		_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
		__UpperCAmelCase       =							ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
		_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
		from .tf_formatter import TFFormatter

		_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
		__UpperCAmelCase       =							ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
		_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
		from .jax_formatter import JaxFormatter

		_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
		__UpperCAmelCase       =							ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
		_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])


def  _snake_case       (					A	)						->   Optional[str]:
							if format_type in _FORMAT_TYPES_ALIASES:
														return _FORMAT_TYPES_ALIASES[format_type]
							else:
														return format_type






def  _snake_case       (					A			,   **A	)						->   Formatter:
							lowerCAmelCase__	       =     get_format_type_from_alias(A	)
							if format_type in _FORMAT_TYPES:
														return _FORMAT_TYPES[format_type](**A	)
							if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
														raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
							else:
														raise ValueError(
														    F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got '{format_type}'"""	)



'''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))