





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     :     Tuple							=	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
     import pyspark







@dataclass
class     __lowerCAmelCase	(   datasets.BuilderConfig):
      _lowercase    : Optional[datasets.Features]            =      None
def 					_A	(		SCREAMING_SNAKE_CASE					:       "pyspark.sql.DataFrame"	,	SCREAMING_SNAKE_CASE					:       List[int]	,	):





  """simple docstring"""



  import pyspark

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

  return generate_fn







class     __lowerCAmelCase	(   _BaseExamplesIterable):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=None		,				)       ->						Any:



        '''simple docstring'''




        a__     : Dict  =df
        a__     : Tuple  =partition_order or range(self.df.rdd.getNumPartitions()							)
        a__     : Any  =_generate_iterable_examples(self.df		,				self.partition_order							)







      def __iter__(   self							)       ->						Dict:



        '''simple docstring'''




        yield from self.generate_examples_fn()







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						"SparkExamplesIterable":



        '''simple docstring'''




        a__     : Any  =list(range(self.df.rdd.getNumPartitions()							)							)
        generator.shuffle(lowerCAmelCase__							)
        return SparkExamplesIterable(self.df		,				partition_order=lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						"SparkExamplesIterable":



        '''simple docstring'''




        a__     : str  =self.split_shard_indices_by_worker(lowerCAmelCase__		,				lowerCAmelCase__							)
        return SparkExamplesIterable(self.df		,				partition_order=lowerCAmelCase__							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.partition_order							)







class     __lowerCAmelCase	(   datasets.DatasetBuilder):
      _lowercase    : List[Any]            =      SparkConfig







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Any:



        '''simple docstring'''




        import pyspark

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

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







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''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__							)
          a__     : List[str]  =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:
          a__     : List[str]  =(
              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        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        import pyspark

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

        a__     : Dict  =self.df.count()
        a__     : Union[str, Any]  =df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
        # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
        a__     : Dict  =(
            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
        )
        a__     : List[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.
          a__     : Tuple  =min(lowerCAmelCase__		,				int(approx_total_size / max_shard_size							)							)
          a__     : List[Any]  =self.df.repartition(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Iterable[Tuple[int, bool, Union[int, tuple]]]:



        '''simple docstring'''




        import pyspark

        a__     : Optional[Any]  =ParquetWriter if file_format == "parquet" else ArrowWriter
        a__     : Dict  =os.path.join(self._working_dir		,				os.path.basename(lowerCAmelCase__							)							) if self._working_dir else fpath
        a__     : 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.
        a__     : Tuple  =self.config.features
        a__     : Optional[Any]  =self._writer_batch_size
        a__     : Dict  =self._fs.storage_options

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

          if writer._num_bytes > 0:
            a__ ,							a__     : 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(lowerCAmelCase__							)							):
              a__     : List[str]  =os.path.join(os.path.dirname(lowerCAmelCase__							)		,				os.path.basename(lowerCAmelCase__							)							)
              shutil.move(lowerCAmelCase__		,				lowerCAmelCase__							)

        a__     : Optional[Any]  =(
            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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = "arrow"		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Dict:



        '''simple docstring'''




        self._validate_cache_dir()

        a__     : Optional[Any]  =convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
        self._repartition_df_if_needed(lowerCAmelCase__							)
        a__     : Optional[Any]  =not is_remote_filesystem(self._fs							)
        a__     : Dict  =os.path.join if is_local else posixpath.join

        a__     : List[str]  ="-TTTTT-SSSSS-of-NNNNN"
        a__     : Union[str, Any]  =F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
        a__     : Union[str, Any]  =path_join(self._output_dir		,				lowerCAmelCase__							)

        a__     : Union[str, Any]  =0
        a__     : List[Any]  =0
        a__     : Dict  =0
        a__     : Optional[Any]  =[]
        a__     : int  =[]

        for task_id, content in self._prepare_split_single(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							):
          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )     : List[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(lowerCAmelCase__							)

        a__     : str  =total_num_examples
        a__     : int  =total_num_bytes

        # should rename everything at the end
        logger.debug(F'''Renaming {total_shards} shards.'''							)
        if total_shards > 1:
          a__     : Dict  =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.
          a__     : List[str]  =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}'''							)		,				)

          a__     : Optional[Any]  =[]
          a__     : Any  =0
          for i in range(len(lowerCAmelCase__							)							):
            a__ ,							a__     : List[Any]  =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
          a__     : List[str]  =0
          a__     : Optional[Any]  =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        _lowercase  (   self		,				lowerCAmelCase__		,				)       ->						SparkExamplesIterable:



        '''simple docstring'''




        return SparkExamplesIterable(self.df							)













from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase     :     Union[str, Any]							=	1.054571817E-34  # unit of ℏ : J * s

UpperCAmelCase     :     Union[str, Any]							=	3E8  # unit of c : m * s^-1
def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  if (force, area, distance).count(0						) != 1:
    raise ValueError("One and only one argument must be 0"						)
  if force < 0:
    raise ValueError("Magnitude of force can not be negative"						)
  if distance < 0:
    raise ValueError("Distance can not be negative"						)
  if area < 0:
    raise ValueError("Area can not be negative"						)
  if force == 0:
    a__     : Tuple  =(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        240 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    a__     : Any  =(240 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    a__     : List[str]  =(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
    ) ** (1 / 4)
    return {"distance": distance}
  raise ValueError("One and only one argument must be 0"						)


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()






