from __future__ import annotations


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     set(__UpperCamelCase ), [start]

      while stack:
            SCREAMING_SNAKE_CASE_      =     stack.pop()
            explored.add(__UpperCamelCase )
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v] ):
                  if adj not in explored:
                        stack.append(__UpperCamelCase )
      return explored


A   :     Optional[Any]       =				{
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(depth_first_search(G, "A"))



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


A   :     Tuple       =				datasets.utils.logging.get_logger(__name__)


@dataclass
class      lowerCamelCase    (datasets.BuilderConfig    ):
  """simple docstring"""







  lowerCamelCase__   =  None
  lowerCamelCase__   =  "utf-8"
  lowerCamelCase__   =  None
  lowerCamelCase__   =  None
  lowerCamelCase__   =  True  # deprecated
  lowerCamelCase__   =  None  # deprecated
  lowerCamelCase__   =  1_0 << 2_0  # 10MB
  lowerCamelCase__   =  None



class      lowerCamelCase    (datasets.ArrowBasedBuilder    ):
  """simple docstring"""







  lowerCamelCase__   =  JsonConfig







  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        if self.config.block_size is not None:
              logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"      )
              SCREAMING_SNAKE_CASE_      =     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       :				List[str]     ,   __magic_name__       :				str      )     ->		Tuple:
        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}'''      )
        SCREAMING_SNAKE_CASE_      =     dl_manager.download_and_extract(self.config.data_files      )
        if isinstance(__magic_name__     ,   (str, list, tuple)      ):
              SCREAMING_SNAKE_CASE_      =     data_files
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={"files": files}      )]
        SCREAMING_SNAKE_CASE_      =     []
        for split_name, files in data_files.items():
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              splits.append(datasets.SplitGenerator(name=__magic_name__     ,   gen_kwargs={"files": files}      )      )
        return splits







  def       __A   (							self       :				str     ,   __magic_name__       :				pa.Table      )     ->		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      ):
                    SCREAMING_SNAKE_CASE_      =     self.config.features.arrow_schema.field(__magic_name__      ).type
                    SCREAMING_SNAKE_CASE_      =     pa_table.append_column(__magic_name__     ,   pa.array([None] * len(__magic_name__      )     ,   type=__magic_name__      )      )
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              SCREAMING_SNAKE_CASE_      =     table_cast(__magic_name__     ,   self.config.features.arrow_schema      )
        return pa_table







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				List[str]      )     ->		int:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__      )      ):
              # 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(__magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                          SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )

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

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

              # If the file has one json object per line
              else:
                    with open(__magic_name__     ,   "rb"      ) as f:
                          SCREAMING_SNAKE_CASE_      =     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
                          SCREAMING_SNAKE_CASE_      =     max(self.config.chunksize // 32     ,   16 << 10      )
                          SCREAMING_SNAKE_CASE_      =     (
                              self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                          )
                          while True:
                                SCREAMING_SNAKE_CASE_      =     f.read(self.config.chunksize      )
                                if not batch:
                                      break
                                # Finish current line
                                try:
                                      batch += f.readline()
                                except (AttributeError, io.UnsupportedOperation):
                                      batch += readline(__magic_name__      )
                                # PyArrow only accepts utf-8 encoded bytes
                                if self.config.encoding != "utf-8":
                                      SCREAMING_SNAKE_CASE_      =     batch.decode(self.config.encoding     ,   errors=__magic_name__      ).encode("utf-8"      )
                                try:
                                      while True:
                                            try:
                                                  SCREAMING_SNAKE_CASE_      =     paj.read_json(
                                                      io.BytesIO(__magic_name__      )     ,   read_options=paj.ReadOptions(block_size=__magic_name__      )      )
                                                  break
                                            except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                  if (
                                                      isinstance(__magic_name__     ,   pa.ArrowInvalid      )
                                                      and "straddling" not in str(__magic_name__      )
                                                      or block_size > len(__magic_name__      )
                                                  ):
                                                        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(__magic_name__      )} 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(
                                                __magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                                                  SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )
                                      except json.JSONDecodeError:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                            raise e
                                      # If possible, parse the file as a list of json objects and exit the loop
                                      if isinstance(__magic_name__     ,   __magic_name__      ):  # list is the only sequence type supported in JSON
                                            try:
                                                  SCREAMING_SNAKE_CASE_      =     set().union(*[row.keys() for row in dataset]      )
                                                  SCREAMING_SNAKE_CASE_      =     {col: [row.get(__magic_name__      ) for row in dataset] for col in keys}
                                                  SCREAMING_SNAKE_CASE_      =     pa.Table.from_pydict(__magic_name__      )
                                            except (pa.ArrowInvalid, AttributeError) as e:
                                                  logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                                  raise ValueError(F'''Not able to read records in the JSON file at {file}.'''      ) from None
                                            yield file_idx, self._cast_table(__magic_name__      )
                                            break
                                      else:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {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(__magic_name__      )
                                batch_idx += 1


