




'''simple docstring'''


import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   np.inf

						def set_batch_size(a							) -> None:
												nonlocal batch_size
												if isinstance(a    ,	a							):
																		__snake_case						 =   min(a    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS							)
												elif isinstance(a    ,	a							):
																		__snake_case						 =   min(a    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS							)
												elif isinstance(a    ,	a							) and feature.dtype == "binary":
																		__snake_case						 =   min(a    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS							)

						_visit(a    ,	a							)

						return None if batch_size is np.inf else batch_size
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  NestedDataStructureLike[PathLike]       ,		__lowerCAmelCase     :  Optional[NamedSplit] = None       ,		__lowerCAmelCase     :  Optional[Features] = None       ,		__lowerCAmelCase     :  str = None       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  Optional[int] = None       ,		**__lowerCAmelCase     :  Tuple       ,		):
							super().__init__(
							    __lowerCAmelCase       ,		split=__lowerCAmelCase       ,		features=__lowerCAmelCase       ,		cache_dir=__lowerCAmelCase       ,		keep_in_memory=__lowerCAmelCase       ,		streaming=__lowerCAmelCase       ,		num_proc=__lowerCAmelCase       ,		**__lowerCAmelCase       ,		)
							__snake_case						 =   path_or_paths if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ) else {self.split: path_or_paths}
							__snake_case						 =   _PACKAGED_DATASETS_MODULES['parquet'][1]
							__snake_case						 =   Parquet(
							    cache_dir=__lowerCAmelCase       ,		data_files=__lowerCAmelCase       ,		features=__lowerCAmelCase       ,		hash=__lowerCAmelCase       ,		**__lowerCAmelCase       ,		)




	def   lowercase__   (       self     :  Tuple      ):
							# Build iterable dataset
							if self.streaming:
													__snake_case						 =   self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
													__snake_case						 =   None
													__snake_case						 =   None
													__snake_case						 =   None
													__snake_case						 =   None

													self.builder.download_and_prepare(
													    download_config=__lowerCAmelCase       ,		download_mode=__lowerCAmelCase       ,		verification_mode=__lowerCAmelCase       ,		base_path=__lowerCAmelCase       ,		num_proc=self.num_proc       ,		)
													__snake_case						 =   self.builder.as_dataset(
													    split=self.split       ,		verification_mode=__lowerCAmelCase       ,		in_memory=self.keep_in_memory      )
							return dataset





class 						a_				:




	def __init__(       self     :  Dict       ,		__lowerCAmelCase     :  Dataset       ,		__lowerCAmelCase     :  Union[PathLike, BinaryIO]       ,		__lowerCAmelCase     :  Optional[int] = None       ,		**__lowerCAmelCase     :  Union[str, Any]       ,		):
							__snake_case						 =   dataset
							__snake_case						 =   path_or_buf
							__snake_case						 =   batch_size or get_writer_batch_size(dataset.features      )
							__snake_case						 =   parquet_writer_kwargs




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

							if isinstance(self.path_or_buf       ,		(str, bytes, os.PathLike)      ):
													with open(self.path_or_buf       ,		'wb+'      ) as buffer:
																			__snake_case						 =   self._write(file_obj=__lowerCAmelCase       ,		batch_size=__lowerCAmelCase       ,		**self.parquet_writer_kwargs      )
							else:
													__snake_case						 =   self._write(file_obj=self.path_or_buf       ,		batch_size=__lowerCAmelCase       ,		**self.parquet_writer_kwargs      )
							return written




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  BinaryIO       ,		__lowerCAmelCase     :  int       ,		**__lowerCAmelCase     :  Optional[Any]      ):
							__snake_case						 =   0
							__snake_case						 =   parquet_writer_kwargs.pop('path_or_buf'       ,		__lowerCAmelCase      )
							__snake_case						 =   self.dataset.features.arrow_schema

							__snake_case						 =   pq.ParquetWriter(__lowerCAmelCase       ,		schema=__lowerCAmelCase       ,		**__lowerCAmelCase      )

							for offset in logging.tqdm(
							    range(0       ,		len(self.dataset      )       ,		__lowerCAmelCase      )       ,		unit='ba'       ,		disable=not logging.is_progress_bar_enabled()       ,		desc='Creating parquet from Arrow format'       ,		):
													__snake_case						 =   query_table(
													    table=self.dataset._data       ,		key=slice(__lowerCAmelCase       ,		offset + batch_size      )       ,		indices=self.dataset._indices if self.dataset._indices is not None else None       ,		)
													writer.write_table(__lowerCAmelCase      )
													written += batch.nbytes
							writer.close()
							return written







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [0] * len(a							)
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   0

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

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

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

						if cnt != len(a							):
												print('Cycle exists'							)
						else:
												print(a							)


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






topological_sort(graph)

