
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				=			datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
   import pyspark






@dataclass
class     UpperCAmelCase						(		datasets.BuilderConfig					):
   A__   :						Optional[datasets.Features]			=	None



def      UpperCamelCase   (       __lowerCamelCase	:					"pyspark.sql.DataFrame"							,   __lowerCamelCase	:					List[int]							,   ):
       import pyspark

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

       return generate_fn






class     UpperCAmelCase						(		_BaseExamplesIterable					):



   def __init__(self						:   str       ,		snake_case__						:   "pyspark.sql.DataFrame"       ,		snake_case__						:   Optional[Any]=None       ,		)						->      List[str]:


          '''simple docstring'''


          snake_case			:  int     							= df
          snake_case			:  Union[str, Any]     							= partition_order or range(self.df.rdd.getNumPartitions()					)
          snake_case			:  Tuple     							= _generate_iterable_examples(self.df       ,		self.partition_order					)



   def __iter__(self						:   Any					)						->      Union[str, Any]:


          '''simple docstring'''


          yield from self.generate_examples_fn()



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   np.random.Generator					)						->      "SparkExamplesIterable":


          '''simple docstring'''


          snake_case			:  Dict     							= list(range(self.df.rdd.getNumPartitions()					)					)
          generator.shuffle(snake_case__					)
          return SparkExamplesIterable(self.df       ,		partition_order=snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   int       ,		snake_case__						:   int					)						->      "SparkExamplesIterable":


          '''simple docstring'''


          snake_case			:  Dict     							= self.split_shard_indices_by_worker(snake_case__       ,		snake_case__					)
          return SparkExamplesIterable(self.df       ,		partition_order=snake_case__					)



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      int:


          '''simple docstring'''


          return len(self.partition_order					)






class     UpperCAmelCase						(		datasets.DatasetBuilder					):
   A__   :						Optional[Any]			=	SparkConfig



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


          '''simple docstring'''


          import pyspark

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

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Tuple:


          '''simple docstring'''


          def create_cache_and_write_probe(snake_case__						:   Optional[Any]					):
                 # 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=snake_case__					)
                 snake_case			:  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(snake_case__       ,		"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:
                 snake_case			:  Optional[int]     							= (
                     self._spark.sparkContext.parallelize(range(1					)       ,		1					).mapPartitions(snake_case__					).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 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Dict:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   datasets.download.download_manager.DownloadManager					)						->      Dict:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   List[str]					)						->      Optional[int]:


          '''simple docstring'''


          import pyspark

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

          snake_case			:  Union[str, Any]     							= self.df.count()
          snake_case			:  int     							= 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.
          snake_case			:  Union[str, Any]     							= (
              self.df.limit(snake_case__					)
              .repartition(1					)
              .mapInArrow(snake_case__       ,		"batch_bytes: long"					)
              .agg(pyspark.sql.functions.sum("batch_bytes"					).alias("sample_bytes"					)					)
              .collect()[0]
              .sample_bytes
              / sample_num_rows
          )
          snake_case			:  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.
                 snake_case			:  Dict     							= min(snake_case__       ,		int(approx_total_size / max_shard_size					)					)
                 snake_case			:  Optional[Any]     							= self.df.repartition(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str       ,		snake_case__						:   str       ,		snake_case__						:   int       ,		)						->      Iterable[Tuple[int, bool, Union[int, tuple]]]:


          '''simple docstring'''


          import pyspark

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

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

                 if writer._num_bytes > 0:
                        snake_case							,   snake_case			:  int     							= 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(snake_case__					)					):
                               snake_case			:  List[Any]     							= os.path.join(os.path.dirname(snake_case__					)       ,		os.path.basename(snake_case__					)					)
                               shutil.move(snake_case__       ,		snake_case__					)

          snake_case			:  Optional[Any]     							= (
              self.df.mapInArrow(snake_case__       ,		"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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   "datasets.SplitGenerator"       ,		snake_case__						:   str = "arrow"       ,		snake_case__						:   Optional[Union[str, int]] = None       ,		snake_case__						:   Optional[int] = None       ,		**snake_case__						:   Tuple       ,		)						->      int:


          '''simple docstring'''


          self._validate_cache_dir()

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

          snake_case			:  int     							= "-TTTTT-SSSSS-of-NNNNN"
          snake_case			:  Optional[Any]     							= f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
          snake_case			:  Optional[int]     							= path_join(self._output_dir       ,		snake_case__					)

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

          for task_id, content in self._prepare_split_single(snake_case__       ,		snake_case__       ,		snake_case__					):
                 (
                     (
                     snake_case
                 )							,   (
                     snake_case
                 )							,   (
                     snake_case
                 )							,   (
                     snake_case
                 )							,   
                 )			:  Tuple     							= 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(snake_case__					)

          snake_case			:  Tuple     							= total_num_examples
          snake_case			:  Optional[int]     							= total_num_bytes

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

                 # use the -SSSSS-of-NNNNN pattern
                 def _rename_shard(
                     snake_case__						:   int       ,		snake_case__						:   int       ,		snake_case__						:   int       ,		):
                        rename(
                            snake_case__       ,		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}"""					)       ,		)

                 snake_case			:  Tuple     							= []
                 snake_case			:  Any     							= 0
                 for i in range(len(snake_case__					)					):
                        snake_case							,   snake_case			:  Any     							= task_id_and_num_shards[i]
                        for shard_id in range(snake_case__					):
                               args.append([task_id, shard_id, global_shard_id]					)
                               global_shard_id += 1
                 self._spark.sparkContext.parallelize(snake_case__       ,		len(snake_case__					)					).map(lambda snake_case__					: _rename_shard(*snake_case__					)					).collect()
          else:
                 # don't use any pattern
                 snake_case			:  int     							= 0
                 snake_case			:  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(snake_case__       ,		""					)       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   "datasets.SplitGenerator"       ,		)						->      SparkExamplesIterable:


          '''simple docstring'''


          return SparkExamplesIterable(self.df					)







class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   str = ""       ,		snake_case__						:   bool = False					)						->      None:


          '''simple docstring'''


          snake_case			:  dict[str, RadixNode]     							= {}

          # A node will be a leaf if the tree contains its word
          snake_case			:  int     							= is_leaf

          snake_case			:  Dict     							= prefix



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str					)						->      tuple[str, str, str]:


          '''simple docstring'''


          snake_case			:  Any     							= 0
          for q, w in zip(self.prefix       ,		snake_case__					):
                 if q != w:
                        break

                 x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   list[str]					)						->      None:


          '''simple docstring'''


          for word in words:
                 self.insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str					)						->      None:


          '''simple docstring'''


          if self.prefix == word:
                 snake_case			:  str     							= True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
                 snake_case			:  Optional[int]     							= RadixNode(prefix=snake_case__       ,		is_leaf=snake_case__					)

          else:
                 snake_case			:  Dict     							= self.nodes[word[0]]
                 snake_case							,   snake_case							,   snake_case			:  int     							= incoming_node.match(
                     snake_case__					)

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                        self.nodes[matching_string[0]].insert(snake_case__					)

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                        snake_case			:  Tuple     							= remaining_prefix

                        snake_case			:  Dict     							= self.nodes[matching_string[0]]
                        snake_case			:  Tuple     							= RadixNode(snake_case__       ,		snake_case__					)
                        snake_case			:  List[str]     							= aux_node

                        if remaining_word == "":
                               snake_case			:  Tuple     							= True
                        else:
                               self.nodes[matching_string[0]].insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[int]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                        return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                        return incoming_node.find(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                        return incoming_node.delete(snake_case__					)
                 else:
                        # If it is not a leaf, we don't have to delete
                        if not incoming_node.is_leaf:
                               return False
                        else:
                               # We delete the nodes if no edges go from it
                               if len(incoming_node.nodes					) == 0:
                                      del self.nodes[word[0]]
                                      # We merge the current node with its only child
                                      if len(self.nodes					) == 1 and not self.is_leaf:
                                             snake_case			:  List[Any]     							= list(self.nodes.values()					)[0]
                                             snake_case			:  Optional[int]     							= merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             snake_case			:  str     							= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                               elif len(incoming_node.nodes					) > 1:
                                      snake_case			:  List[Any]     							= False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      snake_case			:  Any     							= list(incoming_node.nodes.values()					)[0]
                                      snake_case			:  Any     							= merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      snake_case			:  Union[str, Any]     							= merging_node.nodes

                               return True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int = 0					)						->      None:


          '''simple docstring'''


          if self.prefix != "":
                 print("-" * height       ,		self.prefix       ,		"  (leaf)" if self.is_leaf else ""					)

          for value in self.nodes.values():
                 value.print_tree(height + 1					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= "banana bananas bandana band apple all beast".split()
       snake_case			:  Tuple     							= RadixNode()
       root.insert_many(__lowerCamelCase       )

       assert all(root.find(__lowerCamelCase       ) for word in words       )
       assert not root.find("bandanas"       )
       assert not root.find("apps"       )
       root.delete("all"       )
       assert not root.find("all"       )
       root.delete("banana"       )
       assert not root.find("banana"       )
       assert root.find("bananas"       )

       return True


def      UpperCamelCase   (       ):
       assert test_trie()


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= RadixNode()
       snake_case			:  List[str]     							= "banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(__lowerCamelCase       )

       print("Words:"							,   __lowerCamelCase       )
       print("Tree:"       )
       root.print_tree()


if __name__ == "__main__":
   main()





