




import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_lowerCamelCase      			=						datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark



@dataclass
class       a					(       datasets.BuilderConfig      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[datasets.Features]       		=  None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						"pyspark.sql.DataFrame"	,					__UpperCamelCase      :						List[int]	,					)  ->							List[Any]:
    import pyspark

    def generate_fn():
        UpperCAmelCase_										=					df.select('''*'''	,					pyspark.sql.functions.spark_partition_id().alias('''part_id'''				)				)
        for partition_id in partition_order:
            UpperCAmelCase_										=					df_with_partition_id.select('''*'''				).where(f'part_id = {partition_id}'				).drop('''part_id'''				)
            UpperCAmelCase_										=					partition_df.collect()
            UpperCAmelCase_										=					0
            for row in rows:
                yield f'{partition_id}_{row_id}', row.asDict()
                row_id += 1

    return generate_fn



class       a					(       _BaseExamplesIterable      ):

     '''simple docstring'''





     def __init__(     self       :      int ,   __snake_case       :      "pyspark.sql.DataFrame" ,   __snake_case       :      Optional[int]=None ,   ):
         UpperCAmelCase_										=					df
         UpperCAmelCase_										=					partition_order or range(self.df.rdd.getNumPartitions()    )
         UpperCAmelCase_										=					_generate_iterable_examples(self.df ,   self.partition_order    )
     def __iter__(     self       :      List[str]    ):
         yield from self.generate_examples_fn()
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      np.random.Generator    ):
         UpperCAmelCase_										=					list(range(self.df.rdd.getNumPartitions()    )    )
         generator.shuffle(__snake_case    )
         return SparkExamplesIterable(self.df ,   partition_order=__snake_case    )
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      int ,   __snake_case       :      int    ):
         UpperCAmelCase_										=					self.split_shard_indices_by_worker(__snake_case ,   __snake_case    )
         return SparkExamplesIterable(self.df ,   partition_order=__snake_case    )




     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return len(self.partition_order    )



class       a					(       datasets.DatasetBuilder      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  SparkConfig
     def __init__(     self       :      int ,   __snake_case       :      "pyspark.sql.DataFrame" ,   __snake_case       :      str = None ,   __snake_case       :      str = None ,   **__snake_case       :      Tuple ,   ):
         import pyspark

         UpperCAmelCase_										=					pyspark.sql.SparkSession.builder.getOrCreate()
         UpperCAmelCase_										=					df
         UpperCAmelCase_										=					working_dir

         super().__init__(
             cache_dir=__snake_case ,   config_name=str(self.df.semanticHash()    ) ,   **__snake_case ,   )
     def   lowerCamelCase_       (     self       :      int    ):
         # Returns the path of the created file.
         def create_cache_and_write_probe(__snake_case       :      Union[str, Any]    ):
             # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
             # already exist.
             os.makedirs(self._cache_dir ,   exist_ok=__snake_case    )
             UpperCAmelCase_										=					os.path.join(self._cache_dir ,   '''fs_test''' + uuid.uuida().hex    )
             # Opening the file in append mode will create a new file unless it already exists, in which case it will not
             # change the file contents.
             open(__snake_case ,   '''a'''    )
             return [probe_file]

         if self._spark.conf.get('''spark.master''' ,   ''''''    ).startswith('''local'''    ):
             return

         # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
         # accessible to the driver.
         # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
         if self._cache_dir:
             UpperCAmelCase_										=					(
                 self._spark.sparkContext.parallelize(range(1    ) ,   1    ).mapPartitions(__snake_case    ).collect()
             )
             if os.path.isfile(probe[0]    ):
                 return

         raise ValueError(
             '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''    )
     def   lowerCamelCase_       (     self       :      int    ):
         return datasets.DatasetInfo(features=self.config.features    )
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      datasets.download.download_manager.DownloadManager    ):
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN    )]
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Union[str, Any]    ):
         import pyspark

         def get_arrow_batch_size(__snake_case       :      str    ):
             for batch in it:
                 yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}    )

         UpperCAmelCase_										=					self.df.count()
         UpperCAmelCase_										=					df_num_rows if df_num_rows <= 1_00 else 1_00
         # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
         UpperCAmelCase_										=					(
             self.df.limit(__snake_case    )
             .repartition(1    )
             .mapInArrow(__snake_case ,   '''batch_bytes: long'''    )
             .agg(pyspark.sql.functions.sum('''batch_bytes'''    ).alias('''sample_bytes'''    )    )
             .collect()[0]
             .sample_bytes
             / sample_num_rows
         )
         UpperCAmelCase_										=					approx_bytes_per_row * df_num_rows
         if approx_total_size > max_shard_size:
             # Make sure there is at least one row per partition.
             UpperCAmelCase_										=					min(__snake_case ,   int(approx_total_size / max_shard_size    )    )
             UpperCAmelCase_										=					self.df.repartition(__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      str ,   __snake_case       :      str ,   __snake_case       :      int ,   ):
         import pyspark

         UpperCAmelCase_										=					ParquetWriter if file_format == '''parquet''' else ArrowWriter
         UpperCAmelCase_										=					os.path.join(self._working_dir ,   os.path.basename(__snake_case    )    ) if self._working_dir else fpath
         UpperCAmelCase_										=					file_format == '''parquet'''

         # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
         # pickling the SparkContext.
         UpperCAmelCase_										=					self.config.features
         UpperCAmelCase_										=					self._writer_batch_size
         UpperCAmelCase_										=					self._fs.storage_options

         def write_arrow(__snake_case       :      Union[str, Any]    ):
             # Within the same SparkContext, no two task attempts will share the same attempt ID.
             UpperCAmelCase_										=					pyspark.TaskContext().taskAttemptId()
             UpperCAmelCase_										=					next(__snake_case ,   __snake_case    )
             if first_batch is None:
                 # Some partitions might not receive any data.
                 return pa.RecordBatch.from_arrays(
                     [[task_id], [0], [0]] ,   names=['''task_id''', '''num_examples''', '''num_bytes'''] ,   )
             UpperCAmelCase_										=					0
             UpperCAmelCase_										=					writer_class(
                 features=__snake_case ,   path=working_fpath.replace('''SSSSS''' ,   F'{shard_id:05d}'    ).replace('''TTTTT''' ,   F'{task_id:05d}'    ) ,   writer_batch_size=__snake_case ,   storage_options=__snake_case ,   embed_local_files=__snake_case ,   )
             UpperCAmelCase_										=					pa.Table.from_batches([first_batch]    )
             writer.write_table(__snake_case    )
             for batch in it:
                 if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                     UpperCAmelCase_			,							UpperCAmelCase_										=					writer.finalize()
                     writer.close()
                     yield pa.RecordBatch.from_arrays(
                         [[task_id], [num_examples], [num_bytes]] ,   names=['''task_id''', '''num_examples''', '''num_bytes'''] ,   )
                     shard_id += 1
                     UpperCAmelCase_										=					writer_class(
                         features=writer._features ,   path=working_fpath.replace('''SSSSS''' ,   F'{shard_id:05d}'    ).replace('''TTTTT''' ,   F'{task_id:05d}'    ) ,   writer_batch_size=__snake_case ,   storage_options=__snake_case ,   embed_local_files=__snake_case ,   )
                 UpperCAmelCase_										=					pa.Table.from_batches([batch]    )
                 writer.write_table(__snake_case    )

             if writer._num_bytes > 0:
                 UpperCAmelCase_			,							UpperCAmelCase_										=					writer.finalize()
                 writer.close()
                 yield pa.RecordBatch.from_arrays(
                     [[task_id], [num_examples], [num_bytes]] ,   names=['''task_id''', '''num_examples''', '''num_bytes'''] ,   )

             if working_fpath != fpath:
                 for file in os.listdir(os.path.dirname(__snake_case    )    ):
                     UpperCAmelCase_										=					os.path.join(os.path.dirname(__snake_case    ) ,   os.path.basename(__snake_case    )    )
                     shutil.move(__snake_case ,   __snake_case    )

         UpperCAmelCase_										=					(
             self.df.mapInArrow(__snake_case ,   '''task_id: long, num_examples: long, num_bytes: long'''    )
             .groupBy('''task_id'''    )
             .agg(
                 pyspark.sql.functions.sum('''num_examples'''    ).alias('''total_num_examples'''    ) ,   pyspark.sql.functions.sum('''num_bytes'''    ).alias('''total_num_bytes'''    ) ,   pyspark.sql.functions.count('''num_bytes'''    ).alias('''num_shards'''    ) ,   pyspark.sql.functions.collect_list('''num_examples'''    ).alias('''shard_lengths'''    ) ,   )
             .collect()
         )
         for row in stats:
             yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      "datasets.SplitGenerator" ,   __snake_case       :      str = "arrow" ,   __snake_case       :      Optional[Union[str, int]] = None ,   __snake_case       :      Optional[int] = None ,   **__snake_case       :      int ,   ):
         self._validate_cache_dir()

         UpperCAmelCase_										=					convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE    )
         self._repartition_df_if_needed(__snake_case    )
         UpperCAmelCase_										=					not is_remote_filesystem(self._fs    )
         UpperCAmelCase_										=					os.path.join if is_local else posixpath.join

         UpperCAmelCase_										=					'''-TTTTT-SSSSS-of-NNNNN'''
         UpperCAmelCase_										=					F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
         UpperCAmelCase_										=					path_join(self._output_dir ,   __snake_case    )

         UpperCAmelCase_										=					0
         UpperCAmelCase_										=					0
         UpperCAmelCase_										=					0
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					[]

         for task_id, content in self._prepare_split_single(__snake_case ,   __snake_case ,   __snake_case    ):
             (
                 (
                 UpperCAmelCase_
             )			,							(
                 UpperCAmelCase_
             )			,							(
                 UpperCAmelCase_
             )			,							(
                 UpperCAmelCase_
             )			,							
             )										=					content
             if num_bytes > 0:
                 total_num_examples += num_examples
                 total_num_bytes += num_bytes
                 total_shards += num_shards
                 task_id_and_num_shards.append((task_id, num_shards)    )
                 all_shard_lengths.extend(__snake_case    )

         UpperCAmelCase_										=					total_num_examples
         UpperCAmelCase_										=					total_num_bytes

         # should rename everything at the end
         logger.debug(F'Renaming {total_shards} shards.'    )
         if total_shards > 1:
             UpperCAmelCase_										=					all_shard_lengths

             # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
             # pickling error due to pickling the SparkContext.
             UpperCAmelCase_										=					self._fs

             # use the -SSSSS-of-NNNNN pattern
             def _rename_shard(
                 __snake_case       :      int ,   __snake_case       :      int ,   __snake_case       :      int ,   ):
                 rename(
                     __snake_case ,   fpath.replace('''SSSSS''' ,   F'{shard_id:05d}'    ).replace('''TTTTT''' ,   F'{task_id:05d}'    ) ,   fpath.replace('''TTTTT-SSSSS''' ,   F'{global_shard_id:05d}'    ).replace('''NNNNN''' ,   F'{total_shards:05d}'    ) ,   )

             UpperCAmelCase_										=					[]
             UpperCAmelCase_										=					0
             for i in range(len(__snake_case    )    ):
                 UpperCAmelCase_			,							UpperCAmelCase_										=					task_id_and_num_shards[i]
                 for shard_id in range(__snake_case    ):
                     args.append([task_id, shard_id, global_shard_id]    )
                     global_shard_id += 1
             self._spark.sparkContext.parallelize(__snake_case ,   len(__snake_case    )    ).map(lambda __snake_case    : _rename_shard(*__snake_case    )    ).collect()
         else:
             # don't use any pattern
             UpperCAmelCase_										=					0
             UpperCAmelCase_										=					task_id_and_num_shards[0][0]
             self._rename(
                 fpath.replace('''SSSSS''' ,   F'{shard_id:05d}'    ).replace('''TTTTT''' ,   F'{task_id:05d}'    ) ,   fpath.replace(__snake_case ,   ''''''    ) ,   )




     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      "datasets.SplitGenerator" ,   ):
         return SparkExamplesIterable(self.df    )











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))





