
"""simple docstring"""
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


lowercase_       :						List[str]     					=	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
      import pyspark
@dataclass
class 			UpperCamelCase      (							datasets.BuilderConfig  ):
     A__      =			None





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Dict,						)  ->    Tuple:
  import pyspark

  def generate_fn():
    _SCREAMING_SNAKE_CASE   :				int							    = df.select("*",						pyspark.sql.functions.spark_partition_id().alias("part_id"				)				)
    for partition_id in partition_order:
      _SCREAMING_SNAKE_CASE   :				str							    = df_with_partition_id.select("*"				).where(f'''part_id = {partition_id}'''				).drop("part_id"				)
      _SCREAMING_SNAKE_CASE   :				Tuple							    = partition_df.collect()
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 0
      for row in rows:
        yield f'''{partition_id}_{row_id}''', row.asDict()
        row_id += 1

  return generate_fn
class 			UpperCamelCase      (							_BaseExamplesIterable  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__=None  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = df
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = partition_order or range(self.df.rdd.getNumPartitions()				)
       _SCREAMING_SNAKE_CASE   :				Any							    = _generate_iterable_examples(self.df  ,     self.partition_order				)
     def __iter__(     self				):




       """simple docstring"""


       yield from self.generate_examples_fn()
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = list(range(self.df.rdd.getNumPartitions()				)				)
       generator.shuffle(_A				)
       return SparkExamplesIterable(self.df  ,     partition_order=_A				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.split_shard_indices_by_worker(_A  ,     _A				)
       return SparkExamplesIterable(self.df  ,     partition_order=_A				)




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return len(self.partition_order				)
class 			UpperCamelCase      (							datasets.DatasetBuilder  ):
     A__      =			SparkConfig
     def __init__(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       import pyspark

       _SCREAMING_SNAKE_CASE   :				Dict							    = pyspark.sql.SparkSession.builder.getOrCreate()
       _SCREAMING_SNAKE_CASE   :				List[str]							    = df
       _SCREAMING_SNAKE_CASE   :				int							    = working_dir

       super().__init__(
           cache_dir=_A  ,     config_name=str(self.df.semanticHash()				)  ,     **_A  ,     )
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       def create_cache_and_write_probe(snake_case__				):
         # 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=_A				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = 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(_A  ,     "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:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (
             self._spark.sparkContext.parallelize(range(1				)  ,     1				).mapPartitions(_A				).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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.DatasetInfo(features=self.config.features				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       import pyspark

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

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




       """simple docstring"""


       import pyspark

       _SCREAMING_SNAKE_CASE   :				Dict							    = ParquetWriter if file_format == "parquet" else ArrowWriter
       _SCREAMING_SNAKE_CASE   :				List[str]							    = os.path.join(self._working_dir  ,     os.path.basename(_A				)				) if self._working_dir else fpath
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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.
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.config.features
       _SCREAMING_SNAKE_CASE   :				Any							    = self._writer_batch_size
       _SCREAMING_SNAKE_CASE   :				int							    = self._fs.storage_options

       def write_arrow(snake_case__				):
         # Within the same SparkContext, no two task attempts will share the same attempt ID.
         _SCREAMING_SNAKE_CASE   :				int							    = pyspark.TaskContext().taskAttemptId()
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = next(_A  ,     _A				)
         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"]  ,     )
         _SCREAMING_SNAKE_CASE   :				str							    = 0
         _SCREAMING_SNAKE_CASE   :				List[str]							    = writer_class(
             features=_A  ,     path=working_fpath.replace("SSSSS"  ,     F'''{shard_id:05d}'''				).replace("TTTTT"  ,     F'''{task_id:05d}'''				)  ,     writer_batch_size=_A  ,     storage_options=_A  ,     embed_local_files=_A  ,     )
         _SCREAMING_SNAKE_CASE   :				Any							    = pa.Table.from_batches([first_batch]				)
         writer.write_table(_A				)
         for batch in it:
           if max_shard_size is not None and writer._num_bytes >= max_shard_size:
             _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = 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
             _SCREAMING_SNAKE_CASE   :				List[str]							    = writer_class(
                 features=writer._features  ,     path=working_fpath.replace("SSSSS"  ,     F'''{shard_id:05d}'''				).replace("TTTTT"  ,     F'''{task_id:05d}'''				)  ,     writer_batch_size=_A  ,     storage_options=_A  ,     embed_local_files=_A  ,     )
           _SCREAMING_SNAKE_CASE   :				Tuple							    = pa.Table.from_batches([batch]				)
           writer.write_table(_A				)

         if writer._num_bytes > 0:
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = 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(_A				)				):
             _SCREAMING_SNAKE_CASE   :				Tuple							    = os.path.join(os.path.dirname(_A				)  ,     os.path.basename(_A				)				)
             shutil.move(_A  ,     _A				)

       _SCREAMING_SNAKE_CASE   :				str							    = (
           self.df.mapInArrow(_A  ,     "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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = "arrow"  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       self._validate_cache_dir()

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE				)
       self._repartition_df_if_needed(_A				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = not is_remote_filesystem(self._fs				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = os.path.join if is_local else posixpath.join

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = "-TTTTT-SSSSS-of-NNNNN"
       _SCREAMING_SNAKE_CASE   :				Tuple							    = F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = path_join(self._output_dir  ,     _A				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 0
       _SCREAMING_SNAKE_CASE   :				Dict							    = 0
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 0
       _SCREAMING_SNAKE_CASE   :				int							    = []
       _SCREAMING_SNAKE_CASE   :				Any							    = []

       for task_id, content in self._prepare_split_single(_A  ,     _A  ,     _A				):
         (
             (
             _SCREAMING_SNAKE_CASE	
         )    ,		(
             _SCREAMING_SNAKE_CASE	
         )    ,		(
             _SCREAMING_SNAKE_CASE	
         )    ,		(
             _SCREAMING_SNAKE_CASE	
         )    ,			
         )   :				Union[str, Any]							    = 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(_A				)

       _SCREAMING_SNAKE_CASE   :				str							    = total_num_examples
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = total_num_bytes

       # should rename everything at the end
       logger.debug(F'''Renaming {total_shards} shards.'''				)
       if total_shards > 1:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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.
         _SCREAMING_SNAKE_CASE   :				str							    = self._fs

         # use the -SSSSS-of-NNNNN pattern
         def _rename_shard(
             snake_case__  ,     snake_case__  ,     snake_case__  ,     ):
           rename(
               _A  ,     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}'''				)  ,     )

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




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     ):




       """simple docstring"""


       return SparkExamplesIterable(self.df				)





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



