


"""simple docstring"""







def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      bool:
  '''simple docstring'''







  if graph[path[curr_ind - 1]][next_ver] == 0:
    return False

  # 2. Validate that next vertex is not already in path
  return not any(vertex == next_ver for vertex in path       )


def      lowercase			(				A_  ,   A_  ,   A_       )->      bool:
  '''simple docstring'''







  if curr_ind == len(A_       ):
    # return whether path exists between current and starting vertices
    return graph[path[curr_ind - 1]][path[0]] == 1

  # Recursive Step
  for next_ver in range(0  ,   len(A_       )       ):
    if valid_connection(A_  ,   A_  ,   A_  ,   A_       ):
      # Insert current vertex  into path as next transition
      a						:		List[Any]      							=			next_ver
      # Validate created path
      if util_hamilton_cycle(A_  ,   A_  ,   curr_ind + 1       ):
        return True
      # Backtrack
      a						:		List[str]      							=			-1
  return False







def      lowercase			(				A_  ,   A_ = 0       )->      list[int]:
  '''simple docstring'''







  a						:		Dict      							=			[-1] * (len(A_       ) + 1)
  # initialize start and end of path with starting index
  a						:		Union[str, Any]      							=			start_index
  # evaluate and if we find answer return path either return empty array
  return path if util_hamilton_cycle(A_  ,   A_  ,   1       ) else []











from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



