


'''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


__A :  Any    							=				datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark






@dataclass
class        __snake_case	(       datasets.BuilderConfig):







    """simple docstring"""



    lowercase       			= None







def     UpperCamelCase_      (   A__ :       "pyspark.sql.DataFrame"							,   A__ :       List[int]							,   ):
  '''simple docstring'''





  import pyspark

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

  return generate_fn






class        __snake_case	(       _BaseExamplesIterable):







    """simple docstring"""



    def __init__(       self   :  int   ,						lowerCamelCase   :  "pyspark.sql.DataFrame"   ,						lowerCamelCase   :  List[Any]=None   ,						)       ->  str:
      lowerCAmelCase_      :				Any      =       df
      lowerCAmelCase_      :				Any      =       partition_order or range(self.df.rdd.getNumPartitions()		)
      lowerCAmelCase_      :				Optional[int]      =       _generate_iterable_examples(self.df   ,						self.partition_order		)

    def __iter__(       self   :  str		)       ->  Any:
      yield from self.generate_examples_fn()

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  np.random.Generator		)       ->  List[str]:
      lowerCAmelCase_      :				str      =       list(range(self.df.rdd.getNumPartitions()		)		)
      generator.shuffle(_UpperCAmelCase		)
      return SparkExamplesIterable(self.df   ,						partition_order=_UpperCAmelCase		)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  int		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.split_shard_indices_by_worker(_UpperCAmelCase   ,						_UpperCAmelCase		)
      return SparkExamplesIterable(self.df   ,						partition_order=_UpperCAmelCase		)





    @property
    def __lowercase       (       self   :  Any		)       ->  Dict:
      return len(self.partition_order		)







class        __snake_case	(       datasets.DatasetBuilder):







    """simple docstring"""



    lowercase       			= SparkConfig

    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  "pyspark.sql.DataFrame"   ,						lowerCamelCase   :  str = None   ,						lowerCamelCase   :  str = None   ,						**lowerCamelCase   :  int   ,						)       ->  Any:
      import pyspark

      lowerCAmelCase_      :				Optional[int]      =       pyspark.sql.SparkSession.builder.getOrCreate()
      lowerCAmelCase_      :				List[str]      =       df
      lowerCAmelCase_      :				Any      =       working_dir

      super().__init__(
          cache_dir=_UpperCAmelCase   ,						config_name=str(self.df.semanticHash()		)   ,						**_UpperCAmelCase   ,						)

    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:






      def create_cache_and_write_probe(lowerCamelCase   :  int		):
        # 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=_UpperCAmelCase		)
        lowerCAmelCase_      :				Optional[Any]      =       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(_UpperCAmelCase   ,						"""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:
        lowerCAmelCase_      :				List[Any]      =       (
            self._spark.sparkContext.parallelize(range(1		)   ,						1		).mapPartitions(_UpperCAmelCase		).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 __lowercase       (       self   :  str		)       ->  int:
      return datasets.DatasetInfo(features=self.config.features		)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  datasets.download.download_manager.DownloadManager		)       ->  List[Any]:
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]

    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  int		)       ->  List[Any]:
      import pyspark

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

      lowerCAmelCase_      :				Dict      =       self.df.count()
      lowerCAmelCase_      :				Optional[Any]      =       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.
      lowerCAmelCase_      :				List[Any]      =       (
          self.df.limit(_UpperCAmelCase		)
          .repartition(1		)
          .mapInArrow(_UpperCAmelCase   ,						"""batch_bytes: long"""		)
          .agg(pyspark.sql.functions.sum("""batch_bytes"""		).alias("""sample_bytes"""		)		)
          .collect()[0]
          .sample_bytes
          / sample_num_rows
      )
      lowerCAmelCase_      :				Optional[Any]      =       approx_bytes_per_row * df_num_rows
      if approx_total_size > max_shard_size:
        # Make sure there is at least one row per partition.
        lowerCAmelCase_      :				Union[str, Any]      =       min(_UpperCAmelCase   ,						int(approx_total_size / max_shard_size		)		)
        lowerCAmelCase_      :				Optional[Any]      =       self.df.repartition(_UpperCAmelCase		)

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  int   ,						)       ->  Dict:
      import pyspark

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

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

        if writer._num_bytes > 0:
          lowerCAmelCase_,							lowerCAmelCase_      :				List[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(_UpperCAmelCase		)		):
            lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(os.path.dirname(_UpperCAmelCase		)   ,						os.path.basename(_UpperCAmelCase		)		)
            shutil.move(_UpperCAmelCase   ,						_UpperCAmelCase		)

      lowerCAmelCase_      :				Union[str, Any]      =       (
          self.df.mapInArrow(_UpperCAmelCase   ,						"""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 __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  "datasets.SplitGenerator"   ,						lowerCamelCase   :  str = "arrow"   ,						lowerCamelCase   :  Optional[Union[str, int]] = None   ,						lowerCamelCase   :  Optional[int] = None   ,						**lowerCamelCase   :  Any   ,						)       ->  Union[str, Any]:
      self._validate_cache_dir()

      lowerCAmelCase_      :				Union[str, Any]      =       convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
      self._repartition_df_if_needed(_UpperCAmelCase		)
      lowerCAmelCase_      :				int      =       not is_remote_filesystem(self._fs		)
      lowerCAmelCase_      :				int      =       os.path.join if is_local else posixpath.join

      lowerCAmelCase_      :				str      =       """-TTTTT-SSSSS-of-NNNNN"""
      lowerCAmelCase_      :				Optional[int]      =       F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
      lowerCAmelCase_      :				int      =       path_join(self._output_dir   ,						_UpperCAmelCase		)

      lowerCAmelCase_      :				List[str]      =       0
      lowerCAmelCase_      :				List[Any]      =       0
      lowerCAmelCase_      :				int      =       0
      lowerCAmelCase_      :				Optional[Any]      =       []
      lowerCAmelCase_      :				List[Any]      =       []

      for task_id, content in self._prepare_split_single(_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase		):
        (
            (
            lowerCAmelCase_     
        ),							(
            lowerCAmelCase_     
        ),							(
            lowerCAmelCase_     
        ),							(
            lowerCAmelCase_     
        ),							     
        )      :				Optional[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(_UpperCAmelCase		)

      lowerCAmelCase_      :				Any      =       total_num_examples
      lowerCAmelCase_      :				Optional[Any]      =       total_num_bytes

      # should rename everything at the end
      logger.debug(F'Renaming {total_shards} shards.'		)
      if total_shards > 1:
        lowerCAmelCase_      :				Tuple      =       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.
        lowerCAmelCase_      :				Any      =       self._fs

        # use the -SSSSS-of-NNNNN pattern
        def _rename_shard(
            lowerCamelCase   :  int   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  int   ,						):
          rename(
              _UpperCAmelCase   ,						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}'		)   ,						)

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





    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  "datasets.SplitGenerator"   ,						)       ->  List[Any]:
      return SparkExamplesIterable(self.df		)








'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__A :  Any    							=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A :  int    							=				{
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A :  List[Any]    							=				{
    "unc-nlp/lxmert-base-uncased": 512,
}

__A :  Optional[Any]    							=				{
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= VOCAB_FILES_NAMES
    lowercase       			= PRETRAINED_VOCAB_FILES_MAP
    lowercase       			= PRETRAINED_INIT_CONFIGURATION
    lowercase       			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase       			= LxmertTokenizer

    def __init__(       self   :  Dict   ,						lowerCamelCase   :  Tuple=None   ,						lowerCamelCase   :  List[str]=None   ,						lowerCamelCase   :  Dict=True   ,						lowerCamelCase   :  List[Any]="[UNK]"   ,						lowerCamelCase   :  Tuple="[SEP]"   ,						lowerCamelCase   :  str="[PAD]"   ,						lowerCamelCase   :  Any="[CLS]"   ,						lowerCamelCase   :  str="[MASK]"   ,						lowerCamelCase   :  Dict=True   ,						lowerCamelCase   :  Union[str, Any]=None   ,						**lowerCamelCase   :  Optional[int]   ,						)       ->  Optional[Any]:
      super().__init__(
          lowerCamelCase   ,						tokenizer_file=lowerCamelCase   ,						do_lower_case=lowerCamelCase   ,						unk_token=lowerCamelCase   ,						sep_token=lowerCamelCase   ,						pad_token=lowerCamelCase   ,						cls_token=lowerCamelCase   ,						mask_token=lowerCamelCase   ,						tokenize_chinese_chars=lowerCamelCase   ,						strip_accents=lowerCamelCase   ,						**lowerCamelCase   ,						)

      lowerCAmelCase_      :				Optional[Any]      =       json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
      if (
          normalizer_state.get("""lowercase"""   ,						lowerCamelCase		) != do_lower_case
          or normalizer_state.get("""strip_accents"""   ,						lowerCamelCase		) != strip_accents
          or normalizer_state.get("""handle_chinese_chars"""   ,						lowerCamelCase		) != tokenize_chinese_chars
      ):
        lowerCAmelCase_      :				Tuple      =       getattr(lowerCamelCase   ,						normalizer_state.pop("""type"""		)		)
        lowerCAmelCase_      :				Optional[Any]      =       do_lower_case
        lowerCAmelCase_      :				Dict      =       strip_accents
        lowerCAmelCase_      :				List[Any]      =       tokenize_chinese_chars
        lowerCAmelCase_      :				List[Any]      =       normalizer_class(**lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       do_lower_case

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[Any]=None		)       ->  int:
      lowerCAmelCase_      :				Any      =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  List[int]   ,						lowerCamelCase   :  Optional[List[int]] = None		)       ->  List[int]:
      lowerCAmelCase_      :				str      =       [self.sep_token_id]
      lowerCAmelCase_      :				Optional[int]      =       [self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep		) * [0]
      return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]





    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[str] = None		)       ->  Tuple[str]:
      lowerCAmelCase_      :				int      =       self._tokenizer.model.save(lowerCamelCase   ,						name=lowerCamelCase		)
      return tuple(lowerCamelCase		)



