






'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class _lowercase			(       _lowercase	):




       def __init__(							self:       List[str]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int] = None    ,							UpperCamelCase__:       List[Any] = None    ,							UpperCamelCase__:       Dict = None    ,							UpperCamelCase__:       Tuple = False    ,							UpperCamelCase__:       List[Any] = False    ,							UpperCamelCase__:       str = None    ,							UpperCamelCase__:       Optional[Any] = None    ,							**UpperCamelCase__:       Dict    ,							):
              super().__init__(
                  A__    ,							split=A__    ,							features=A__    ,							cache_dir=A__    ,							keep_in_memory=A__    ,							streaming=A__    ,							num_proc=A__    ,							**A__    ,							)
              lowerCamelCase__      :			Tuple 						= field
              lowerCamelCase__      :			Optional[int] 						= path_or_paths if isinstance(A__    ,							A__  ) else {self.split: path_or_paths}
              lowerCamelCase__      :			List[Any] 						= Json(
                  cache_dir=A__    ,							data_files=A__    ,							features=A__    ,							field=A__    ,							**A__    ,							)




       def 	lowerCamelCase_       (							self:       Any  ):
              # Build iterable dataset
              if self.streaming:
                     lowerCamelCase__      :			Dict 						= self.builder.as_streaming_dataset(split=self.split  )
              # Build regular (map-style) dataset
              else:
                     lowerCamelCase__      :			str 						= None
                     lowerCamelCase__      :			str 						= None
                     lowerCamelCase__      :			Union[str, Any] 						= None
                     lowerCamelCase__      :			Any 						= None

                     self.builder.download_and_prepare(
                         download_config=A__    ,							download_mode=A__    ,							verification_mode=A__    ,							base_path=A__    ,							num_proc=self.num_proc    ,							)
                     lowerCamelCase__      :			Union[str, Any] 						= self.builder.as_dataset(
                         split=self.split    ,							verification_mode=A__    ,							in_memory=self.keep_in_memory  )
              return dataset



class _lowercase			:




       def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[Any] = None    ,							UpperCamelCase__:       Any = None    ,							**UpperCamelCase__:       List[Any]    ,							):
              if num_proc is not None and num_proc <= 0:
                     raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''  )

              lowerCamelCase__      :			int 						= dataset
              lowerCamelCase__      :			Any 						= path_or_buf
              lowerCamelCase__      :			Union[str, Any] 						= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
              lowerCamelCase__      :			str 						= num_proc
              lowerCamelCase__      :			Union[str, Any] 						= """utf-8"""
              lowerCamelCase__      :			List[Any] 						= to_json_kwargs




       def 	lowerCamelCase_       (							self:       Tuple  ):
              lowerCamelCase__      :			List[Any] 						= self.to_json_kwargs.pop("""path_or_buf"""    ,							A__  )
              lowerCamelCase__      :			List[Any] 						= self.to_json_kwargs.pop("""orient"""    ,							"""records"""  )
              lowerCamelCase__      :			Optional[Any] 						= self.to_json_kwargs.pop("""lines"""    ,							True if orient == """records""" else False  )
              lowerCamelCase__      :			Optional[int] 						= self.to_json_kwargs.pop("""index"""    ,							False if orient in ["""split""", """table"""] else True  )
              lowerCamelCase__      :			int 						= self.to_json_kwargs.pop("""compression"""    ,							A__  )

              if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                     raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''  )

              if isinstance(self.path_or_buf    ,							(str, bytes, os.PathLike)  ):
                     with fsspec.open(self.path_or_buf    ,							"""wb"""    ,							compression=A__  ) as buffer:
                            lowerCamelCase__      :			Optional[Any] 						= self._write(file_obj=A__    ,							orient=A__    ,							lines=A__    ,							index=A__    ,							**self.to_json_kwargs  )
              else:
                     if compression:
                            raise NotImplementedError(
                                F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
                                """ was passed. Please provide a local path instead."""  )
                     lowerCamelCase__      :			int 						= self._write(
                         file_obj=self.path_or_buf    ,							orient=A__    ,							lines=A__    ,							index=A__    ,							**self.to_json_kwargs  )
              return written




       def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       str  ):
              lowerCamelCase__      :			int 						= args

              lowerCamelCase__      :			Union[str, Any] 						= query_table(
                  table=self.dataset.data    ,							key=slice(A__    ,							offset + self.batch_size  )    ,							indices=self.dataset._indices    ,							)
              lowerCamelCase__      :			List[Any] 						= batch.to_pandas().to_json(
                  path_or_buf=A__    ,							orient=A__    ,							lines=A__    ,							index=A__    ,							**A__  )
              if not json_str.endswith("""\n"""  ):
                     json_str += "\n"
              return json_str.encode(self.encoding  )




       def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Optional[Any]    ,							**UpperCamelCase__:       Union[str, Any]    ,							):
              lowerCamelCase__      :			List[str] 						= 0

              if self.num_proc is None or self.num_proc == 1:
                     for offset in logging.tqdm(
                         range(0    ,							len(self.dataset  )    ,							self.batch_size  )    ,							unit="""ba"""    ,							disable=not logging.is_progress_bar_enabled()    ,							desc="""Creating json from Arrow format"""    ,							):
                            lowerCamelCase__      :			Optional[int] 						= self._batch_json((offset, orient, lines, index, to_json_kwargs)  )
                            written += file_obj.write(A__  )
              else:
                     lowerCamelCase__      :			Optional[int] 						= len(self.dataset  ), self.batch_size
                     with multiprocessing.Pool(self.num_proc  ) as pool:
                            for json_str in logging.tqdm(
                                pool.imap(
                                    self._batch_json    ,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0    ,							A__    ,							A__  )]    ,							)    ,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size    ,							unit="""ba"""    ,							disable=not logging.is_progress_bar_enabled()    ,							desc="""Creating json from Arrow format"""    ,							):
                                   written += file_obj.write(A__  )

              return written









'''simple docstring'''







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			str 						= [False] * len(UpperCamelCase   )
							lowerCamelCase__      :			str 						= [-1] * len(UpperCamelCase   )

							def dfs(UpperCamelCase       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[int] 						= True
														lowerCamelCase__      :			Union[str, Any] 						= c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase       ,    1 - c   )

							for i in range(len(UpperCamelCase   )   ):
														if not visited[i]:
																					dfs(UpperCamelCase       ,    0   )

							for i in range(len(UpperCamelCase   )   ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_A			:    int					  ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}



print(check_bipartite_dfs(graph))

