



'''simple docstring'''





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


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


@dataclass
class 				snake_case (datasets.BuilderConfig   ):
       lowerCAmelCase__  :Optional[datasets.Features]						   =					None
       lowerCAmelCase__  :str						   =					"utf-8"
       lowerCAmelCase__  :Optional[str]						   =					None
       lowerCAmelCase__  :Optional[str]						   =					None
       lowerCAmelCase__  :bool						   =					True  # deprecated
       lowerCAmelCase__  :Optional[int]						   =					None  # deprecated
       lowerCAmelCase__  :int						   =					10 << 20  # 10MB
       lowerCAmelCase__  :Optional[bool]						   =					None





class 				snake_case (datasets.ArrowBasedBuilder   ):
       lowerCAmelCase__  :List[str]						   =					JsonConfig



       def 							_a      (				self      )  ->							Optional[Any]:
           if self.config.block_size is not None:
               logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"      )
               lowercase__		       =						self.config.block_size
           if self.config.use_threads is not True:
               logger.warning(
                   "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."      )
           if self.config.newlines_in_values is not None:
               raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"      )
           return datasets.DatasetInfo(features=self.config.features      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           if not self.config.data_files:
               raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''      )
           lowercase__		       =						dl_manager.download_and_extract(self.config.data_files      )
           if isinstance(UpperCAmelCase_     ,(str, list, tuple)      ):
               lowercase__		       =						data_files
               if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
                   lowercase__		       =						[files]
               lowercase__		       =						[dl_manager.iter_files(UpperCAmelCase_      ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,gen_kwargs={"files": files}      )]
           lowercase__		       =						[]
           for split_name, files in data_files.items():
               if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
                   lowercase__		       =						[files]
               lowercase__		       =						[dl_manager.iter_files(UpperCAmelCase_      ) for file in files]
               splits.append(datasets.SplitGenerator(name=UpperCAmelCase_     ,gen_kwargs={"files": files}      )      )
           return splits



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							pa.Table:
           if self.config.features is not None:
               # adding missing columns
               for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
                   lowercase__		       =						self.config.features.arrow_schema.field(UpperCAmelCase_      ).type
                   lowercase__		       =						pa_table.append_column(UpperCAmelCase_     ,pa.array([None] * len(UpperCAmelCase_      )     ,type=UpperCAmelCase_      )      )
               # more expensive cast to support nested structures with keys in a different order
               # allows str <-> int/float or str to Audio for example
               lowercase__		       =						table_cast(UpperCAmelCase_     ,self.config.features.arrow_schema      )
           return pa_table







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							int:
           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_      )      ):
               # If the file is one json object and if we need to look at the list of items in one specific field
               if self.config.field is not None:
                   with open(UpperCAmelCase_     ,encoding=self.config.encoding     ,errors=self.config.encoding_errors      ) as f:
                       lowercase__		       =						json.load(UpperCAmelCase_      )

                   # We keep only the field we are interested in
                   lowercase__		       =						dataset[self.config.field]

                   # We accept two format: a list of dicts or a dict of lists
                   if isinstance(UpperCAmelCase_     ,(list, tuple)      ):
                       lowercase__		       =						set().union(*[row.keys() for row in dataset]      )
                       lowercase__		       =						{col: [row.get(UpperCAmelCase_      ) for row in dataset] for col in keys}
                   else:
                       lowercase__		       =						dataset
                   lowercase__		       =						pa.Table.from_pydict(UpperCAmelCase_      )
                   yield file_idx, self._cast_table(UpperCAmelCase_      )

               # If the file has one json object per line
               else:
                   with open(UpperCAmelCase_     ,"rb"      ) as f:
                       lowercase__		       =						0
                       # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                       # Set a default minimum value of 16kB if the chunk size is really small
                       lowercase__		       =						max(self.config.chunksize // 32     ,16 << 10      )
                       lowercase__		       =						(
                           self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                       )
                       while True:
                           lowercase__		       =						f.read(self.config.chunksize      )
                           if not batch:
                               break
                           # Finish current line
                           try:
                               batch += f.readline()
                           except (AttributeError, io.UnsupportedOperation):
                               batch += readline(UpperCAmelCase_      )
                           # PyArrow only accepts utf-8 encoded bytes
                           if self.config.encoding != "utf-8":
                               lowercase__		       =						batch.decode(self.config.encoding     ,errors=UpperCAmelCase_      ).encode("utf-8"      )
                           try:
                               while True:
                                   try:
                                       lowercase__		       =						paj.read_json(
                                           io.BytesIO(UpperCAmelCase_      )     ,read_options=paj.ReadOptions(block_size=UpperCAmelCase_      )      )
                                       break
                                   except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                       if (
                                           isinstance(UpperCAmelCase_     ,pa.ArrowInvalid      )
                                           and "straddling" not in str(UpperCAmelCase_      )
                                           or block_size > len(UpperCAmelCase_      )
                                       ):
                                           raise
                                       else:
                                           # Increase the block size in case it was too small.
                                           # The block size will be reset for the next file.
                                           logger.debug(
                                               F'''Batch of {len(UpperCAmelCase_      )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''      )
                                           block_size *= 2
                           except pa.ArrowInvalid as e:
                               try:
                                   with open(
                                       UpperCAmelCase_     ,encoding=self.config.encoding     ,errors=self.config.encoding_errors      ) as f:
                                       lowercase__		       =						json.load(UpperCAmelCase_      )
                               except json.JSONDecodeError:
                                   logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCAmelCase_      )}: {e}'''      )
                                   raise e
                               # If possible, parse the file as a list of json objects and exit the loop
                               if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):  # list is the only sequence type supported in JSON
                                   try:
                                       lowercase__		       =						set().union(*[row.keys() for row in dataset]      )
                                       lowercase__		       =						{col: [row.get(UpperCAmelCase_      ) for row in dataset] for col in keys}
                                       lowercase__		       =						pa.Table.from_pydict(UpperCAmelCase_      )
                                   except (pa.ArrowInvalid, AttributeError) as e:
                                       logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCAmelCase_      )}: {e}'''      )
                                       raise ValueError(F'''Not able to read records in the JSON file at {file}.'''      ) from None
                                   yield file_idx, self._cast_table(UpperCAmelCase_      )
                                   break
                               else:
                                   logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCAmelCase_      )}: {e}'''      )
                                   raise ValueError(
                                       F'''Not able to read records in the JSON file at {file}. '''
                                       F'''You should probably indicate the field of the JSON file containing your records. '''
                                       F'''This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. '''
                                       F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                           yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase_      )
                           batch_idx += 1









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




