"""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


__lowerCAmelCase					: Optional[int]      =datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
       import pyspark





@dataclass
class 				_A		( datasets.BuilderConfig			):
      snake_case__    :     Optional[datasets.Features]													=			None






def     UpperCAmelCase__ (       lowerCAmelCase__  :"pyspark.sql.DataFrame"      ,	lowerCAmelCase__  :List[int]      ,	)				->					List[str]:
      '''simple docstring'''


      import pyspark

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

      return generate_fn





class 				_A		( _BaseExamplesIterable			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=None	, ):







            """simple docstring"""



            lowercase														=			df
            lowercase														=			partition_order or range(self.df.rdd.getNumPartitions()		)
            lowercase														=			_generate_iterable_examples(self.df	, self.partition_order		)

      def __iter__(		self		):







            """simple docstring"""



            yield from self.generate_examples_fn()

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			list(range(self.df.rdd.getNumPartitions()		)		)
            generator.shuffle(__lowerCAmelCase		)
            return SparkExamplesIterable(self.df	, partition_order=__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.split_shard_indices_by_worker(__lowerCAmelCase	, __lowerCAmelCase		)
            return SparkExamplesIterable(self.df	, partition_order=__lowerCAmelCase		)







      @property
      def        A__ (		self		):







            """simple docstring"""



            return len(self.partition_order		)






class 				_A		( datasets.DatasetBuilder			):
      snake_case__    :     Union[str, Any]													=			SparkConfig

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            import pyspark

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

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

      def        A__ (		self		):







            """simple docstring"""



            def create_cache_and_write_probe(__lowerCAmelCase		):
                  # 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=__lowerCAmelCase		)
                  lowercase														=			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(__lowerCAmelCase	, """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:
                  lowercase														=			(
                      self._spark.sparkContext.parallelize(range(1		)	, 1		).mapPartitions(__lowerCAmelCase		).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        A__ (		self		):







            """simple docstring"""



            return datasets.DatasetInfo(features=self.config.features		)

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            import pyspark

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

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, ):







            """simple docstring"""



            import pyspark

            lowercase														=			ParquetWriter if file_format == """parquet""" else ArrowWriter
            lowercase														=			os.path.join(self._working_dir	, os.path.basename(__lowerCAmelCase		)		) if self._working_dir else fpath
            lowercase														=			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.
            lowercase														=			self.config.features
            lowercase														=			self._writer_batch_size
            lowercase														=			self._fs.storage_options

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

                  if writer._num_bytes > 0:
                        lowercase							,							lowercase														=			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(__lowerCAmelCase		)		):
                              lowercase														=			os.path.join(os.path.dirname(__lowerCAmelCase		)	, os.path.basename(__lowerCAmelCase		)		)
                              shutil.move(__lowerCAmelCase	, __lowerCAmelCase		)

            lowercase														=			(
                self.df.mapInArrow(__lowerCAmelCase	, """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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = "arrow"	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            self._validate_cache_dir()

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

            lowercase														=			"""-TTTTT-SSSSS-of-NNNNN"""
            lowercase														=			f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
            lowercase														=			path_join(self._output_dir	, __lowerCAmelCase		)

            lowercase														=			0
            lowercase														=			0
            lowercase														=			0
            lowercase														=			[]
            lowercase														=			[]

            for task_id, content in self._prepare_split_single(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):
                  (
                      (
                      lowercase							
                  )							,							(
                      lowercase							
                  )							,							(
                      lowercase							
                  )							,							(
                      lowercase							
                  )							,														
                  )														=			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(__lowerCAmelCase		)

            lowercase														=			total_num_examples
            lowercase														=			total_num_bytes

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

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

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







      def        A__ (		self	, __lowerCAmelCase	, ):







            """simple docstring"""



            return SparkExamplesIterable(self.df		)




"""simple docstring"""






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase					: Union[str, Any]        =subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCAmelCase					: List[str]        =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()

__lowerCAmelCase					: List[str]        ="""|""".join(sys.argv[1:])
__lowerCAmelCase					: Dict        =re.compile(RF"""^({joined_dirs}).*?\.py$""")

__lowerCAmelCase					: int        =[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")



