




import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase      			=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
 from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase      			=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
 if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
  warnings.warn(F"A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.")
 fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							str:
    if "://" in dataset_path:
        UpperCAmelCase_										=					dataset_path.split('''://'''				)[1]
    return dataset_path




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						fsspec.AbstractFileSystem				)  ->							bool:
    if fs is not None and fs.protocol != "file":
        return True
    else:
        return False




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						fsspec.AbstractFileSystem	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						str				)  ->							List[str]:
    UpperCAmelCase_										=					not is_remote_filesystem(__UpperCamelCase				)
    if is_local:
        # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
        shutil.move(fs._strip_protocol(__UpperCamelCase				)	,					fs._strip_protocol(__UpperCamelCase				)				)
    else:
        fs.mv(__UpperCamelCase	,					__UpperCamelCase	,					recursive=__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	)  ->							None:
    if hasattr(fsspec.asyn	,					'''reset_lock'''				):
        # for future fsspec>2022.05.0
        fsspec.asyn.reset_lock()
    else:
        UpperCAmelCase_										=					None
        UpperCAmelCase_										=					None
        UpperCAmelCase_										=					threading.Lock()











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[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=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    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(__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


_lowerCamelCase      			=						CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase      			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_lowerCamelCase      			=						'Le camembert est <mask> :)'


print(fill_mask(masked_input, model, tokenizer, topk=3))





