





'''simple docstring'''

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowercase__		:     Tuple			=							[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]







def    a__							(							lowercase   :	Tuple=True )						->						List[Any]:





     """simple docstring"""

     if with_config:
          return [
              {
                  "testcase_name": d["dataset"] + "/" + d["config_name"],
                  "dataset": d["dataset"],
                  "config_name": d["config_name"],
              }
              for d in DATASETS_ON_HF_GCP
          ]
     else:
          return [
              {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
          ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__magic_name__					)					)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Union[str, Any]									=       None
       _snake_case      :	Dict									=       None





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Any	)       ->	Dict:

            '''simple docstring'''




            with TemporaryDirectory() as tmp_dir:
                 _UpperCamelCase							       =			dataset_module_factory(lowerCAmelCase__			,					cache_dir=lowerCAmelCase__	)

                 _UpperCamelCase							       =			import_main_class(dataset_module.module_path			,					dataset=lowerCAmelCase__	)

                 _UpperCamelCase							       =			builder_cls(
                     cache_dir=lowerCAmelCase__			,					config_name=lowerCAmelCase__			,					hash=dataset_module.hash			,					)

                 _UpperCamelCase							       =			'''/'''.join(
                     [
                         HF_GCP_BASE_URL,
                         builder_instance._relative_data_dir(with_hash=lowerCAmelCase__	).replace(os.sep			,					'''/'''	),
                         config.DATASET_INFO_FILENAME,
                     ]	)
                 _UpperCamelCase							       =			cached_path(lowerCAmelCase__			,					cache_dir=lowerCAmelCase__	)
                 self.assertTrue(os.path.exists(lowerCAmelCase__	)	)







@pytest.mark.integration
def    a__							(							lowercase   :	Any )						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
     _UpperCamelCase							       =			dataset_module_factory('''wikipedia''',   cache_dir=lowercase )
     _UpperCamelCase							       =			import_main_class(dataset_module.module_path )
     _UpperCamelCase							       =			builder_cls(
         cache_dir=lowercase,   config_name='''20220301.frr''',   hash=dataset_module.hash,   )
     # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
     _UpperCamelCase							       =			None
     builder_instance.download_and_prepare()
     _UpperCamelCase							       =			builder_instance.as_dataset()
     assert ds







@pytest.mark.integration
def    a__							(							lowercase   :	Any )						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			dataset_module_factory('''wikipedia''',   cache_dir=lowercase )
     _UpperCamelCase							       =			import_main_class(dataset_module.module_path,   dataset=lowercase )
     _UpperCamelCase							       =			builder_cls(
         cache_dir=lowercase,   config_name='''20220301.frr''',   hash=dataset_module.hash,   )
     _UpperCamelCase							       =			builder_instance.as_streaming_dataset()
     assert ds
     assert isinstance(lowercase,   lowercase )
     assert "train" in ds
     assert isinstance(ds['''train'''],   lowercase )
     assert next(iter(ds['''train'''] ) )










"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

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