
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


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

if TYPE_CHECKING:
			import pyspark





@dataclass
class 	_lowerCAmelCase					(	datasets.BuilderConfig       ):
				_lowercase					=None






def    lowerCamelCase__				(				__lowerCAmelCase  :  "pyspark.sql.DataFrame"    ,							__lowerCAmelCase  :  List[int]    ,							):




			"""simple docstring"""


			import pyspark

			def generate_fn():
						lowerCAmelCase_						=	df.select("*"    ,							pyspark.sql.functions.spark_partition_id().alias("part_id"				)				)
						for partition_id in partition_order:
									lowerCAmelCase_						=	df_with_partition_id.select("*"				).where(F"""part_id = {partition_id}"""				).drop("part_id"				)
									lowerCAmelCase_						=	partition_df.collect()
									lowerCAmelCase_						=	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				,				_UpperCamelCase				,				_UpperCamelCase=None				,				)				->      Dict:
							lowerCAmelCase_						=	df
							lowerCAmelCase_						=	partition_order or range(self.df.rdd.getNumPartitions()     )
							lowerCAmelCase_						=	_generate_iterable_examples(self.df				,				self.partition_order     )




				def __iter__(					self     )				->      List[str]:
							yield from self.generate_examples_fn()




				def      __a						(					self				,				_UpperCamelCase     )				->      "SparkExamplesIterable":
							lowerCAmelCase_						=	list(range(self.df.rdd.getNumPartitions()     )     )
							generator.shuffle(_UpperCamelCase     )
							return SparkExamplesIterable(self.df				,				partition_order=_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      "SparkExamplesIterable":
							lowerCAmelCase_						=	self.split_shard_indices_by_worker(_UpperCamelCase				,				_UpperCamelCase     )
							return SparkExamplesIterable(self.df				,				partition_order=_UpperCamelCase     )




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





class 	_lowerCAmelCase					(	datasets.DatasetBuilder       ):
				_lowercase					=SparkConfig




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      int:
							import pyspark

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

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




				def      __a						(					self     )				->      Optional[int]:
							# Returns the path of the created file.
							def create_cache_and_write_probe(_UpperCamelCase     ):
										# 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=_UpperCamelCase     )
										lowerCAmelCase_						=	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(_UpperCamelCase				,				"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:
										lowerCAmelCase_						=	(
										    self._spark.sparkContext.parallelize(range(1     )				,				1     ).mapPartitions(_UpperCamelCase     ).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     )				->      List[Any]:
							return datasets.DatasetInfo(features=self.config.features     )




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




				def      __a						(					self				,				_UpperCamelCase     )				->      int:
							import pyspark

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

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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				)				->      Iterable[Tuple[int, bool, Union[int, tuple]]]:
							import pyspark

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

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

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

							lowerCAmelCase_						=	(
							    self.df.mapInArrow(_UpperCamelCase				,				"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				,				_UpperCamelCase				,				_UpperCamelCase = "arrow"				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      str:
							self._validate_cache_dir()

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

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

							lowerCAmelCase_						=	0
							lowerCAmelCase_						=	0
							lowerCAmelCase_						=	0
							lowerCAmelCase_						=	[]
							lowerCAmelCase_						=	[]

							for task_id, content in self._prepare_split_single(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     ):
										(
										    (
										    lowerCAmelCase_
										)	,  (
										    lowerCAmelCase_
										)	,  (
										    lowerCAmelCase_
										)	,  (
										    lowerCAmelCase_
										)	,  
										)						=	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(_UpperCamelCase     )

							lowerCAmelCase_						=	total_num_examples
							lowerCAmelCase_						=	total_num_bytes

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

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

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




				def      __a						(					self				,				_UpperCamelCase				,				)				->      SparkExamplesIterable:
							return SparkExamplesIterable(self.df     )





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = ""				):




			"""simple docstring"""


			lowerCAmelCase_						=	url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
			lowerCAmelCase_						=	BeautifulSoup(requests.get(__lowerCAmelCase				).text    ,							"html.parser"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							attrs="titleColumn"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							class_="ratingColumn imdbRating"				)
			return {
			    title.a.text: float(rating.strong.text				)
			    for title, rating in zip(__lowerCAmelCase    ,							__lowerCAmelCase				)
			}






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = "IMDb_Top_250_Movies.csv"				):




			"""simple docstring"""


			lowerCAmelCase_						=	get_imdb_top_aaa_movies()
			with open(__lowerCAmelCase    ,							"w"    ,							newline=""				) as out_file:
						lowerCAmelCase_						=	csv.writer(__lowerCAmelCase				)
						writer.writerow(["Movie title", "IMDb rating"]				)
						for title, rating in movies.items():
									writer.writerow([title, rating]				)


if __name__ == "__main__":
			write_movies()



