




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


_snake_case										=       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark







@dataclass
class 						lowercase							(   datasets.BuilderConfig		):
     _a							 =						None







def 				lowerCAmelCase_	(     snake_case_,snake_case_,):
      import pyspark

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

      return generate_fn







class 						lowercase							(   _BaseExamplesIterable		):

     def __init__(  self		,     _a		,     _a=None		,     )						->							Any:
           _A							:     Optional[Any]			=						df
           _A							:     Tuple			=						partition_order or range(self.df.rdd.getNumPartitions()				)
           _A							:     Tuple			=						_generate_iterable_examples(self.df		,     self.partition_order				)

     def __iter__(  self				)						->							Optional[int]:
           yield from self.generate_examples_fn()

     def 					a__	(  self		,     _a				)						->							List[str]:
           _A							:     List[str]			=						list(range(self.df.rdd.getNumPartitions()				)				)
           generator.shuffle(lowerCAmelCase__				)
           return SparkExamplesIterable(self.df		,     partition_order=lowerCAmelCase__				)

     def 					a__	(  self		,     _a		,     _a				)						->							Optional[Any]:
           _A							:     str			=						self.split_shard_indices_by_worker(lowerCAmelCase__		,     lowerCAmelCase__				)
           return SparkExamplesIterable(self.df		,     partition_order=lowerCAmelCase__				)



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







class 						lowercase							(   datasets.DatasetBuilder		):
     _a							 =						SparkConfig

     def __init__(  self		,     _a		,     _a = None		,     _a = None		,     **_a		,     )						->							Any:
           import pyspark

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

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

     def 					a__	(  self				)						->							str:
           # Returns the path of the created file.
           def create_cache_and_write_probe(_a				):
                 # 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							:     Union[str, 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(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							:     Dict			=						(
                     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				)						->							Tuple:
           return datasets.DatasetInfo(features=self.config.features				)

     def 					a__	(  self		,     _a				)						->							Dict:
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]

     def 					a__	(  self		,     _a				)						->							Optional[Any]:
           import pyspark

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

           _A							:     Optional[Any]			=						self.df.count()
           _A							:     Tuple			=						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.
           _A							:     Optional[int]			=						(
               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							:     Union[str, 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							:     Any			=						min(lowerCAmelCase__		,     int(approx_total_size / max_shard_size				)				)
                 _A							:     Any			=						self.df.repartition(lowerCAmelCase__				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     )						->							str:
           import pyspark

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

           def write_arrow(_a				):
                 # Within the same SparkContext, no two task attempts will share the same attempt ID.
                 _A							:     Tuple			=						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							:     Dict			=						0
                 _A							:     Dict			=						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[str]			=						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							:     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
                             _A							:     Optional[int]			=						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							:     Tuple			=						pa.Table.from_batches([batch]				)
                       writer.write_table(lowerCAmelCase__				)

                 if writer._num_bytes > 0:
                       _A							:     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(lowerCAmelCase__				)				):
                             _A							:     List[Any]			=						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 					a__	(  self		,     _a		,     _a = "arrow"		,     _a = None		,     _a = None		,     **_a		,     )						->							Tuple:
           self._validate_cache_dir()

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

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

           _A							:     List[Any]			=						0
           _A							:     Any			=						0
           _A							:     Union[str, Any]			=						0
           _A							:     Optional[Any]			=						[]
           _A							:     Tuple			=						[]

           for task_id, content in self._prepare_split_single(lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__				):
                 (
                     _A    
                 )							:     List[str]			=						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							:     Any			=						total_num_examples
           _A							:     Any			=						total_num_bytes

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

                 # use the -SSSSS-of-NNNNN pattern
                 def _rename_shard(
                     _a		,     _a		,     _a		,     ):
                       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							:     Tuple			=						[]
                 _A							:     Tuple			=						0
                 for i in range(len(lowerCAmelCase__				)				):
                       _A							:     int			=						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 _a				: _rename_shard(*lowerCAmelCase__				)				).collect()
           else:
                 # don't use any pattern
                 _A							:     Optional[int]			=						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 					a__	(  self		,     _a		,     )						->							Dict:
           return SparkExamplesIterable(self.df				)




def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       [0] * len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       []
						SCREAMING_SNAKE_CASE_: str  		=       []
						SCREAMING_SNAKE_CASE_: List[str]  		=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(_UpperCAmelCase    )    ):
												if indegree[i] == 0:
																		queue.append(_UpperCAmelCase    )

						while queue:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       queue.pop(0    )
												cnt += 1
												topo.append(_UpperCAmelCase    )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(_UpperCAmelCase    )

						if cnt != len(_UpperCAmelCase    ):
												print("Cycle exists"    )
						else:
												print(_UpperCAmelCase    )


# Adjacency List of Graph
lowerCAmelCase							:  Any   	=						{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}



topological_sort(graph)






