from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class 		_lowercase						:
 lowercase				     =   4_2
 lowercase				     =   4_2





class 		_lowercase						:

 def __init__( self	:							Optional[int]   ,      snake_case	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						list[list[Edge]] =					[[] for _ in range(__a			)]
  UpperCamelCase_					:						int =					size

 def __getitem__( self	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  return iter(self._graph[vertex]			)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  return self._size

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Tuple			)   ->						Optional[int]:

  """simple docstring"""





  if weight not in (0, 1):
   raise ValueError('Edge weight must be either 0 or 1.'			)

  if to_vertex < 0 or to_vertex >= self.size:
   raise ValueError('Vertex indexes must be in [0; size).'			)

  self._graph[from_vertex].append(Edge(__a   ,      __a			)			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Any   ,      snake_case	:							str			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						str =					deque([start_vertex]			)
  UpperCamelCase_					:						list[int | None] =					[None] * self.size
  UpperCamelCase_					:						Optional[int] =					0

  while queue:
   UpperCamelCase_					:						Any =					queue.popleft()
   UpperCamelCase_					:						Tuple =					distances[current_vertex]
   if current_distance is None:
    continue

   for edge in self[current_vertex]:
    UpperCamelCase_					:						Optional[Any] =					current_distance + edge.weight
    UpperCamelCase_					:						Optional[Any] =					distances[edge.destination_vertex]
    if (
        isinstance(__a   ,      __a			)
        and new_distance >= dest_vertex_distance
    ):
     continue
    UpperCamelCase_					:						Dict =					new_distance
    if edge.weight == 0:
     queue.appendleft(edge.destination_vertex			)
    else:
     queue.append(edge.destination_vertex			)

  if distances[finish_vertex] is None:
   raise ValueError('No path from start_vertex to finish_vertex.'			)

  return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					__a			:       Optional[Any]					    =  tmp_path / 'file.csv'
					__a			:       Union[str, Any]					    =  textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       str					    =  tmp_path / 'malformed_file.csv'
					__a			:       int					    =  textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20,\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  tmp_path / 'csv_with_image.csv'
					__a			:       Dict					    =  textwrap.dedent(
					    F"""\
        image
        {image_file}
        """   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Union[str, Any]					    =  tmp_path / 'csv_with_label.csv'
					__a			:       Any					    =  textwrap.dedent(
					    '\\n        label\n        good\n        bad\n        good\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






@pytest.fixture
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Dict					    =  tmp_path / 'csv_with_int_list.csv'
					__a			:       Tuple					    =  textwrap.dedent(
					    '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '   )
					with open(_SCREAMING_SNAKE_CASE						,      'w'   ) as f:
										f.write(_SCREAMING_SNAKE_CASE   )
					return str(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       int					    =  Csv()
					__a			:       str					    =  csv._generate_tables([[csv_file, malformed_csv_file]]   )
					with pytest.raises(_SCREAMING_SNAKE_CASE						,      match='Error tokenizing data'   ):
										for _ in generator:
															pass
					assert any(
					    record.levelname == 'ERROR'
					    and 'Failed to read file' in record.message
					    and os.path.basename(_SCREAMING_SNAKE_CASE   ) in record.message
					    for record in caplog.records   )






@require_pil
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1]
					__a			:       Tuple					    =  Csv(encoding='utf-8'						,      features=Features({'image': Image()}   )   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_image]]   )
					__a			:       int					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('image'   ).type == Image()()
					__a			:       Any					    =  pa_table.to_pydict()['image']
					assert generated_content == [{"path": image_file, "bytes": None}]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					with open(_SCREAMING_SNAKE_CASE						,      encoding='utf-8'   ) as f:
										__a			:       Tuple					    =  f.read().splitlines()[1:]
					__a			:       Optional[int]					    =  Csv(encoding='utf-8'						,      features=Features({'label': ClassLabel(names=['good', 'bad']   )}   )   )
					__a			:       List[str]					    =  csv._generate_tables([[csv_file_with_label]]   )
					__a			:       Dict					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('label'   ).type == ClassLabel(names=['good', 'bad']   )()
					__a			:       int					    =  pa_table.to_pydict()['label']
					assert generated_content == [ClassLabel(names=['good', 'bad']   ).straint(_SCREAMING_SNAKE_CASE   ) for label in labels]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  Csv(encoding='utf-8'						,      sep=','						,      converters={'int_list': lambda _SCREAMING_SNAKE_CASE   : [int(_SCREAMING_SNAKE_CASE   ) for i in x.split()]}   )
					__a			:       Any					    =  csv._generate_tables([[csv_file_with_int_list]]   )
					__a			:       Any					    =  pa.concat_tables([table for _, table in generator]   )
					assert pa.types.is_list(pa_table.schema.field('int_list'   ).type   )
					__a			:       Tuple					    =  pa_table.to_pydict()['int_list']
					assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



