


'''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


__UpperCAmelCase      :List[Any]			    =       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark



@dataclass
class      a    (		datasets.BuilderConfig	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[datasets.Features]							       =       None




def      _a     (						_lowercase :     "pyspark.sql.DataFrame"      , _lowercase :     List[int]      , ):





     '''simple docstring'''






     import pyspark

     def generate_fn():
          __UpperCAmelCase     :			List[Any]  							=      df.select('''*'''      , pyspark.sql.functions.spark_partition_id().alias('''part_id'''  )  )
          for partition_id in partition_order:
               __UpperCAmelCase     :			List[Any]  							=      df_with_partition_id.select('''*'''  ).where(F'part_id = {partition_id}'  ).drop('''part_id'''  )
               __UpperCAmelCase     :			Union[str, Any]  							=      partition_df.collect()
               __UpperCAmelCase     :			int  							=      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					:		Optional[int]							,			snake_case					:		Dict							,			snake_case					:		int=None							,			)						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      df
          __UpperCAmelCase     :			Any  							=      partition_order or range(self.df.rdd.getNumPartitions()      )
          __UpperCAmelCase     :			Tuple  							=      _generate_iterable_examples(self.df							,			self.partition_order      )

     def __iter__(    self					:		Dict      )						->							int:
          yield from self.generate_examples_fn()

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Optional[int]      )						->							"SparkExamplesIterable":
          __UpperCAmelCase     :			List[str]  							=      list(range(self.df.rdd.getNumPartitions()      )      )
          generator.shuffle(a__      )
          return SparkExamplesIterable(self.df							,			partition_order=a__      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		str							,			snake_case					:		List[Any]      )						->							"SparkExamplesIterable":
          __UpperCAmelCase     :			Union[str, Any]  							=      self.split_shard_indices_by_worker(a__							,			a__      )
          return SparkExamplesIterable(self.df							,			partition_order=a__      )






     @property
     def 			lowerCamelCase__			(    self					:		int      )						->							int:
          return len(self.partition_order      )



class      a    (		datasets.DatasetBuilder	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       SparkConfig

     def __init__(    self					:		List[Any]							,			snake_case					:		List[str]							,			snake_case					:		Any = None							,			snake_case					:		List[Any] = None							,			**snake_case					:		Any							,			)						->							Optional[Any]:
          import pyspark

          __UpperCAmelCase     :			Any  							=      pyspark.sql.SparkSession.builder.getOrCreate()
          __UpperCAmelCase     :			str  							=      df
          __UpperCAmelCase     :			Union[str, Any]  							=      working_dir

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

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Optional[Any]:




          def create_cache_and_write_probe(snake_case					:		str      ):
               # 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__      )
               __UpperCAmelCase     :			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(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:
               __UpperCAmelCase     :			Optional[int]  							=      (
                   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 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features      )

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int      )						->							Any:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      )]

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Optional[Any]      )						->							Optional[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     :			Any  							=      self.df.count()
          __UpperCAmelCase     :			int  							=      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.
          __UpperCAmelCase     :			List[Any]  							=      (
              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
          )
          __UpperCAmelCase     :			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.
               __UpperCAmelCase     :			Union[str, Any]  							=      min(a__							,			int(approx_total_size / max_shard_size      )      )
               __UpperCAmelCase     :			int  							=      self.df.repartition(a__      )

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		Union[str, Any]							,			snake_case					:		Tuple							,			)						->							Iterable[Tuple[int, bool, Union[int, tuple]]]:
          import pyspark

          __UpperCAmelCase     :			str  							=      ParquetWriter if file_format == '''parquet''' else ArrowWriter
          __UpperCAmelCase     :			List[Any]  							=      os.path.join(self._working_dir							,			os.path.basename(a__      )      ) if self._working_dir else fpath
          __UpperCAmelCase     :			Dict  							=      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     :			int  							=      self.config.features
          __UpperCAmelCase     :			str  							=      self._writer_batch_size
          __UpperCAmelCase     :			Dict  							=      self._fs.storage_options

          def write_arrow(snake_case					:		Optional[Any]      ):
               # Within the same SparkContext, no two task attempts will share the same attempt ID.
               __UpperCAmelCase     :			str  							=      pyspark.TaskContext().taskAttemptId()
               __UpperCAmelCase     :			str  							=      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''']							,			)
               __UpperCAmelCase     :			str  							=      0
               __UpperCAmelCase     :			List[Any]  							=      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__							,			)
               __UpperCAmelCase     :			Union[str, 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:
                         __UpperCAmelCase							,       __UpperCAmelCase     :			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
                         __UpperCAmelCase     :			Tuple  							=      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__							,			)
                    __UpperCAmelCase     :			Optional[Any]  							=      pa.Table.from_batches([batch]      )
                    writer.write_table(a__      )

               if writer._num_bytes > 0:
                    __UpperCAmelCase							,       __UpperCAmelCase     :			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__      )      ):
                         __UpperCAmelCase     :			Optional[int]  							=      os.path.join(os.path.dirname(a__      )							,			os.path.basename(a__      )      )
                         shutil.move(a__							,			a__      )

          __UpperCAmelCase     :			Optional[int]  							=      (
              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 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Optional[int]							,			snake_case					:		List[str] = "arrow"							,			snake_case					:		Optional[int] = None							,			snake_case					:		List[str] = None							,			**snake_case					:		Optional[int]							,			)						->							Dict:
          self._validate_cache_dir()

          __UpperCAmelCase     :			Dict  							=      convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE      )
          self._repartition_df_if_needed(a__      )
          __UpperCAmelCase     :			Tuple  							=      not is_remote_filesystem(self._fs      )
          __UpperCAmelCase     :			Tuple  							=      os.path.join if is_local else posixpath.join

          __UpperCAmelCase     :			str  							=      '''-TTTTT-SSSSS-of-NNNNN'''
          __UpperCAmelCase     :			str  							=      f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
          __UpperCAmelCase     :			str  							=      path_join(self._output_dir							,			a__      )

          __UpperCAmelCase     :			List[Any]  							=      0
          __UpperCAmelCase     :			Tuple  							=      0
          __UpperCAmelCase     :			List[str]  							=      0
          __UpperCAmelCase     :			Optional[Any]  							=      []
          __UpperCAmelCase     :			Optional[int]  							=      []

          for task_id, content in self._prepare_split_single(a__							,			a__							,			a__      ):
               (
                   (
                   __UpperCAmelCase				
               )							,       (
                   __UpperCAmelCase				
               )							,       (
                   __UpperCAmelCase				
               )							,       (
                   __UpperCAmelCase				
               )							,       				
               )     :			int  							=      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__      )

          __UpperCAmelCase     :			int  							=      total_num_examples
          __UpperCAmelCase     :			List[str]  							=      total_num_bytes

          # should rename everything at the end
          logger.debug(f'Renaming {total_shards} shards.'      )
          if total_shards > 1:
               __UpperCAmelCase     :			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.
               __UpperCAmelCase     :			Union[str, Any]  							=      self._fs

               # use the -SSSSS-of-NNNNN pattern
               def _rename_shard(
                   snake_case					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		List[Any]							,			):
                    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}'      )							,			)

               __UpperCAmelCase     :			int  							=      []
               __UpperCAmelCase     :			List[str]  							=      0
               for i in range(len(a__      )      ):
                    __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      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
               __UpperCAmelCase     :			Optional[Any]  							=      0
               __UpperCAmelCase     :			int  							=      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 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Any							,			)						->							SparkExamplesIterable:
          return SparkExamplesIterable(self.df      )



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      :List[Any]			    =       {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       ["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       [
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[str]			    =       [
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      __UpperCAmelCase      :int			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)