





"""simple docstring"""


import math
import sys



class   lowercase_					:

       def __init__(      self  ):






        '''simple docstring'''

        snake_case__     				=					0
        snake_case__     				=					[]
        snake_case__     				=					{}  # To store the pos of node in array

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return self.cur_size == 0

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					self.left(snake_case_  )
        snake_case__     				=					self.right(snake_case_  )
        if lc < self.cur_size and self.array(snake_case_  )[0] < self.array(snake_case_  )[0]:
         snake_case__     				=					lc
        else:
         snake_case__     				=					idx
        if rc < self.cur_size and self.array(snake_case_  )[0] < self.array(snake_case_  )[0]:
         snake_case__     				=					rc
        if smallest != idx:
         self.swap(snake_case_ ,       snake_case_  )
         self.min_heapify(snake_case_  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					self.cur_size
        self.cur_size += 1
        self.array.append((sys.maxsize, tup[1])  )
        self.decrease_key((sys.maxsize, tup[1]) ,       tup[0]  )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.array[0][1]
        snake_case__     				=					self.array[self.cur_size - 1]
        self.cur_size -= 1
        self.min_heapify(1  )
        del self.pos[min_node]
        return min_node

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return 2 * i + 1

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return 2 * i + 2

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return math.floor(i / 2  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					j
        snake_case__     				=					i
        snake_case__     				=					self.array[i]
        snake_case__     				=					self.array[j]
        snake_case__     				=					temp

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					self.pos[tup[1]]
        # assuming the new_d is atmost old_d
        snake_case__     				=					(new_d, tup[1])
        while idx > 0 and self.array[self.par(snake_case_  )][0] > self.array[idx][0]:
         self.swap(snake_case_ ,       self.par(snake_case_  )  )
         snake_case__     				=					self.par(snake_case_  )



class   lowercase_					:

       def __init__(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					{}  # To store graph: u -> (v,w)
        snake_case__     				=					num  # Number of nodes in graph
        # To store the distance from source vertex
        snake_case__     				=					[0] * self.num_nodes
        snake_case__     				=					[-1] * self.num_nodes  # To store the path

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        if u in self.adjList:
         self.adjList[u].append((v, w)  )
        else:
         snake_case__     				=					[(v, w)]

        # Assuming undirected graph
        if v in self.adjList:
         self.adjList[v].append((u, w)  )
        else:
         snake_case__     				=					[(u, w)]

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        for u in self.adjList:
         print(snake_case_ ,       "->" ,       " -> ".join(str(F"{v}({w})"  ) for v, w in self.adjList[u]  )  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					[-1] * self.num_nodes
        # src is the source node
        snake_case__     				=					0
        snake_case__     				=					PriorityQueue()
        q.insert((0, src)  )  # (dist from src, node)
        for u in self.adjList:
         if u != src:
          snake_case__     				=					sys.maxsize  # Infinity
          snake_case__     				=					-1

        while not q.is_empty():
         snake_case__     				=					q.extract_min()  # Returns node with the min dist from source
         # Update the distance of all the neighbours of u and
         # if their prev dist was INFINITY then push them in Q
         for v, w in self.adjList[u]:
          snake_case__     				=					self.dist[u] + w
          if self.dist[v] > new_dist:
           if self.dist[v] == sys.maxsize:
            q.insert((new_dist, v)  )
           else:
            q.decrease_key((self.dist[v], v) ,       snake_case_  )
           snake_case__     				=					new_dist
           snake_case__     				=					u

        # Show the shortest distances from src
        self.show_distances(snake_case_  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        print(F"Distance from node: {src}"  )
        for u in range(self.num_nodes  ):
         print(F"Node {u} has distance: {self.dist[u]}"  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					[]
        snake_case__     				=					0
        snake_case__     				=					dest
        # Backtracking from dest to src
        while self.par[temp] != -1:
         path.append(snake_case_  )
         if temp != src:
          for v, w in self.adjList[temp]:
           if v == self.par[temp]:
            cost += w
            break
         snake_case__     				=					self.par[temp]
        path.append(snake_case_  )
        path.reverse()

        print(F"----Path to reach {dest} from {src}----"  )
        for u in path:
         print(F"{u}" ,       end=" "  )
         if u != dest:
          print("-> " ,       end=""  )

        print("\nTotal cost of path: " ,       snake_case_  )


if __name__ == "__main__":
   a__       :  Union[str, Any]       		=					Graph(9)
   graph.add_edge(0, 1, 4)
   graph.add_edge(0, 7, 8)
   graph.add_edge(1, 2, 8)
   graph.add_edge(1, 7, 1_1)
   graph.add_edge(2, 3, 7)
   graph.add_edge(2, 8, 2)
   graph.add_edge(2, 5, 4)
   graph.add_edge(3, 4, 9)
   graph.add_edge(3, 5, 1_4)
   graph.add_edge(4, 5, 1_0)
   graph.add_edge(5, 6, 2)
   graph.add_edge(6, 7, 1)
   graph.add_edge(6, 8, 6)
   graph.add_edge(7, 8, 7)
   graph.show_graph()
   graph.dijkstra(0)
   graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21








"""simple docstring"""




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

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

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = False   , snake_case_ = False   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(
            snake_case_   , split=snake_case_   , features=snake_case_   , cache_dir=snake_case_   , keep_in_memory=snake_case_   , streaming=snake_case_   , num_proc=snake_case_   , **snake_case_   , )
        A_    :			int									=    path_or_paths if isinstance(snake_case_   , snake_case_    ) else {self.split: path_or_paths}
        A_    :			List[str]									=    Csv(
            cache_dir=snake_case_   , data_files=snake_case_   , features=snake_case_   , **snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.streaming:
          A_    :			List[str]									=    self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
          A_    :			List[Any]									=    None
          A_    :			Optional[int]									=    None
          A_    :			Optional[int]									=    None
          A_    :			List[str]									=    None

          self.builder.download_and_prepare(
              download_config=snake_case_   , download_mode=snake_case_   , verification_mode=snake_case_   , base_path=snake_case_   , num_proc=self.num_proc   , )
          A_    :			List[Any]									=    self.builder.as_dataset(
              split=self.split   , verification_mode=snake_case_   , in_memory=self.keep_in_memory    )
        return dataset


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        if num_proc is not None and num_proc <= 0:
          raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""    )

        A_    :			Optional[int]									=    dataset
        A_    :			List[str]									=    path_or_buf
        A_    :			Optional[int]									=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A_    :			Optional[int]									=    num_proc
        A_    :			Optional[int]									=    'utf-8'
        A_    :			int									=    to_csv_kwargs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.to_csv_kwargs.pop('path_or_buf'   , snake_case_    )
        A_    :			str									=    self.to_csv_kwargs.pop('header'   , snake_case_    )
        A_    :			Optional[Any]									=    self.to_csv_kwargs.pop('index'   , snake_case_    )

        if isinstance(self.path_or_buf   , (str, bytes, os.PathLike)    ):
          with open(self.path_or_buf   , 'wb+'    ) as buffer:
            A_    :			Optional[Any]									=    self._write(file_obj=snake_case_   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        else:
          A_    :			int									=    self._write(file_obj=self.path_or_buf   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        return written

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Union[str, Any]									=    args

        A_    :			Dict									=    query_table(
            table=self.dataset.data   , key=slice(snake_case_   , offset + self.batch_size    )   , indices=self.dataset._indices   , )
        A_    :			List[str]									=    batch.to_pandas().to_csv(
            path_or_buf=snake_case_   , header=header if (offset == 0) else False   , index=snake_case_   , **snake_case_    )
        return csv_str.encode(self.encoding    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    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 CSV from Arrow format'   , ):
            A_    :			Optional[int]									=    self._batch_csv((offset, header, index, to_csv_kwargs)    )
            written += file_obj.write(snake_case_    )

        else:
          A_		,			A_    :			Optional[int]									=    len(self.dataset    ), self.batch_size
          with multiprocessing.Pool(self.num_proc    ) as pool:
            for csv_str in logging.tqdm(
                pool.imap(
                    self._batch_csv   , [(offset, header, index, to_csv_kwargs) for offset in range(0   , snake_case_   , snake_case_    )]   , )   , 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 CSV from Arrow format'   , ):
              written += file_obj.write(snake_case_    )

        return written