






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 		A_			(		lowercase_					,       lowercase_	)							->Union[str, Any]:







 """simple docstring"""

 assert isinstance(lowercase_					,       lowercase_	)
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'					,       [False, True]	)
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  SCREAMING_SNAKE_CASE							  =						CsvDatasetReader(lowercase_					,       cache_dir=lowercase_					,       keep_in_memory=lowercase_	).read()
 _check_csv_dataset(lowercase_					,       lowercase_	)





@pytest.mark.parametrize(
    'features'					,       [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					,       )
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 SCREAMING_SNAKE_CASE							  =						features.copy() if features else default_expected_features
 SCREAMING_SNAKE_CASE							  =						(
     Features({feature: Value(lowercase_	) for feature, dtype in features.items()}	) if features is not None else None
 )
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader(lowercase_					,       features=lowercase_					,       cache_dir=lowercase_	).read()
 _check_csv_dataset(lowercase_					,       lowercase_	)





@pytest.mark.parametrize('split'					,       [None, NamedSplit('train'	), 'train', 'test']	)
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader(lowercase_					,       cache_dir=lowercase_					,       split=lowercase_	).read()
 _check_csv_dataset(lowercase_					,       lowercase_	)
 assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'					,       [str, list]	)
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

 if issubclass(lowercase_					,       lowercase_	):
  SCREAMING_SNAKE_CASE							  =						csv_path
 elif issubclass(lowercase_					,       lowercase_	):
  SCREAMING_SNAKE_CASE							  =						[csv_path]
 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader(lowercase_					,       cache_dir=lowercase_	).read()
 _check_csv_dataset(lowercase_					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_=("train",)	)							->Optional[Any]:







 """simple docstring"""

 assert isinstance(lowercase_					,       lowercase_	)
 for split in splits:
  SCREAMING_SNAKE_CASE							  =						dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'					,       [False, True]	)
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  SCREAMING_SNAKE_CASE							  =						CsvDatasetReader({'train': csv_path}					,       cache_dir=lowercase_					,       keep_in_memory=lowercase_	).read()
 _check_csv_datasetdict(lowercase_					,       lowercase_	)





@pytest.mark.parametrize(
    'features'					,       [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					,       )
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 SCREAMING_SNAKE_CASE							  =						features.copy() if features else default_expected_features
 SCREAMING_SNAKE_CASE							  =						(
     Features({feature: Value(lowercase_	) for feature, dtype in features.items()}	) if features is not None else None
 )
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader({'train': csv_path}					,       features=lowercase_					,       cache_dir=lowercase_	).read()
 _check_csv_datasetdict(lowercase_					,       lowercase_	)





@pytest.mark.parametrize('split'					,       [None, NamedSplit('train'	), 'train', 'test']	)
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Optional[Any]:







 """simple docstring"""

 if split:
  SCREAMING_SNAKE_CASE							  =						{split: csv_path}
 else:
  SCREAMING_SNAKE_CASE							  =						{'train': csv_path, 'test': csv_path}
 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader(lowercase_					,       cache_dir=lowercase_	).read()
 _check_csv_datasetdict(lowercase_					,       lowercase_					,       splits=list(path.keys()	)	)
 assert all(dataset[split].split == split for split in path.keys()	)





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 with open(lowercase_					,       encoding='utf-8'	) as csvfile:
  yield from csv.reader(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						os.path.join(lowercase_					,       'tmp.csv'	)
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader({'train': csv_path}					,       cache_dir=lowercase_	).read()
 CsvDatasetWriter(dataset['train']					,       lowercase_					,       num_proc=1	).write()

 SCREAMING_SNAKE_CASE							  =						iter_csv_file(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						iter_csv_file(lowercase_	)

 for rowa, rowa in zip(lowercase_					,       lowercase_	):
  assert rowa == rowa





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						os.path.join(lowercase_					,       'tmp.csv'	)
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader({'train': csv_path}					,       cache_dir=lowercase_	).read()
 CsvDatasetWriter(dataset['train']					,       lowercase_					,       num_proc=2	).write()

 SCREAMING_SNAKE_CASE							  =						iter_csv_file(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						iter_csv_file(lowercase_	)

 for rowa, rowa in zip(lowercase_					,       lowercase_	):
  assert rowa == rowa





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						tmp_path / 'cache'
 SCREAMING_SNAKE_CASE							  =						os.path.join(lowercase_					,       'tmp.csv'	)
 SCREAMING_SNAKE_CASE							  =						CsvDatasetReader({'train': csv_path}					,       cache_dir=lowercase_	).read()
 with pytest.raises(lowercase_	):
  CsvDatasetWriter(dataset['train']					,       lowercase_					,       num_proc=0	)












import math
import sys
class 					a_:



     """simple docstring"""



     def __init__(						self			:					Optional[int])    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						{}  # To store the pos of node in array





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Optional[int]:

      """simple docstring"""
      return self.cur_size == 0





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					str)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.left(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.right(lowerCAmelCase__)
      if lc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						lc
      else:
       SCREAMING_SNAKE_CASE							  =						idx
      if rc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						rc
      if smallest != idx:
       self.swap(lowerCAmelCase__       ,       lowerCAmelCase__)
       self.min_heapify(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					Tuple)    ->					Optional[Any]:

      """simple docstring"""
      # Inserts a node into the Priority Queue
      SCREAMING_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   __UpperCamelCase		(						self			:					Dict)    ->					Optional[Any]:

      """simple docstring"""
      # Removes and returns the min element at top of priority queue
      SCREAMING_SNAKE_CASE							  =						self.array[0][1]
      SCREAMING_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   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Dict)    ->					int:

      """simple docstring"""
      # returns the index of left child
      return 2 * i + 1





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					List[str])    ->					Optional[Any]:

      """simple docstring"""
      # returns the index of right child
      return 2 * i + 2





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					List[Any])    ->					Dict:

      """simple docstring"""
      # returns the index of parent
      return math.floor(i / 2)





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					List[str]       ,       lowerCAmelCase__			:					Any)    ->					List[str]:

      """simple docstring"""
      # swaps array elements at indices i and j
      # update the pos{}
      SCREAMING_SNAKE_CASE							  =						j
      SCREAMING_SNAKE_CASE							  =						i
      SCREAMING_SNAKE_CASE							  =						self.array[i]
      SCREAMING_SNAKE_CASE							  =						self.array[j]
      SCREAMING_SNAKE_CASE							  =						temp





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					Optional[Any])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      SCREAMING_SNAKE_CASE							  =						(new_d, tup[1])
      while idx > 0 and self.array[self.par(lowerCAmelCase__)][0] > self.array[idx][0]:
       self.swap(lowerCAmelCase__       ,       self.par(lowerCAmelCase__))
       SCREAMING_SNAKE_CASE							  =						self.par(lowerCAmelCase__)

class 					a_:



     """simple docstring"""



     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					str)    ->					str:

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





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					List[Any])    ->					Tuple:

      """simple docstring"""
      #  Edge going from node u to v and v to u with weight w
      # u (w)-> v, v (w) -> u
      # Check if u already in graph
      if u in self.adjList:
       self.adjList[u].append((v, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(v, w)]

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





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Tuple:

      """simple docstring"""
      # u -> v(w)
      for u in self.adjList:
       print(lowerCAmelCase__       ,       '->'       ,       ' -> '.join(str(f'''{v}({w})''') for v, w in self.adjList[u]))





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					List[str])    ->					List[Any]:

      """simple docstring"""
      # Flush old junk values in par[]
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes
      # src is the source node
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						PriorityQueue()
      q.insert((0, src))  # (dist from src, node)
      for u in self.adjList:
       if u != src:
        SCREAMING_SNAKE_CASE							  =						sys.maxsize  # Infinity
        SCREAMING_SNAKE_CASE							  =						-1

      while not q.is_empty():
       SCREAMING_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]:
        SCREAMING_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)       ,       lowerCAmelCase__)
         SCREAMING_SNAKE_CASE							  =						new_dist
         SCREAMING_SNAKE_CASE							  =						u

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





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					List[str])    ->					Tuple:

      """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   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Any)    ->					Optional[int]:

      """simple docstring"""
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
       path.append(lowerCAmelCase__)
       if temp != src:
        for v, w in self.adjList[temp]:
         if v == self.par[temp]:
          cost += w
          break
       SCREAMING_SNAKE_CASE							  =						self.par[temp]
      path.append(lowerCAmelCase__)
      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: '       ,       lowerCAmelCase__)


if __name__ == "__main__":
      __UpperCAmelCase      				=							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




