

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__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		List[str]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  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__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Optional[int]:
  UpperCAmelCase :							Optional[int]								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							Union[str, Any]								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@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__  (	UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		Optional[Any]:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							Optional[Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							List[Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[Any]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							Any								=				CsvDatasetReader(UpperCAmelCase       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    str    )						->		Any:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Any								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							int								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				split=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type'''       ,				[str, list]    )
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Any    )						->		str:
  if issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							Tuple								=				csv_path
  elif issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							List[str]								=				[csv_path]
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Tuple								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    str       ,				UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    int=("train",)    )						->		Optional[int]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  for split in splits:
    UpperCAmelCase :							Optional[Any]								=				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__  (	UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Tuple    )						->		Tuple:
  UpperCAmelCase :							Any								=				tmp_path / '''cache'''
  UpperCAmelCase :							Dict								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							str								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@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__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    Any    )						->		Optional[int]:
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							List[str]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Union[str, Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[str]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Dict:
  if split:
    UpperCAmelCase :							Union[str, Any]								=				{split: csv_path}
  else:
    UpperCAmelCase :							List[str]								=				{'''train''': csv_path, '''test''': csv_path}
  UpperCAmelCase :							str								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							str								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase       ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )

def     a__  (	UpperCAmelCase							:    Optional[Any]    )						->		int:
  with open(UpperCAmelCase       ,				encoding='''utf-8'''    ) as csvfile:
    yield from csv.reader(UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Union[str, Any]    )						->		Union[str, Any]:
  UpperCAmelCase :							int								=				tmp_path / '''cache'''
  UpperCAmelCase :							int								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=1    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							int								=				iter_csv_file(UpperCAmelCase    )

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

def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Dict    )						->		Any:
  UpperCAmelCase :							List[Any]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=2    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							Union[str, Any]								=				iter_csv_file(UpperCAmelCase    )

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






def     a__  (	UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[int]    )						->		Optional[Any]:
  UpperCAmelCase :							Dict								=				tmp_path / '''cache'''
  UpperCAmelCase :							List[Any]								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  with pytest.raises(UpperCAmelCase    ):
    CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=0    )








from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       :     Optional[int]           =							{
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							Dict								=				{start}
  UpperCAmelCase :							Optional[int]								=				[start]
  UpperCAmelCase :							Queue								=				Queue()
  queue.put(UpperCAmelCase    )
  while not queue.empty():
    UpperCAmelCase :							List[Any]								=				queue.get()
    for w in graph[v]:
      if w not in explored:
        explored.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.put(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							str								=				{start}
  UpperCAmelCase :							List[str]								=				[start]
  UpperCAmelCase :							Union[str, Any]								=				deque([start]    )
  while queue:
    UpperCAmelCase :							Dict								=				queue.popleft()
    for child in graph[v]:
      if child not in visited:
        visited.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.append(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    str    )						->		None:
  UpperCAmelCase :							Optional[int]								=				f'''from __main__ import G, {name}'''
  UpperCAmelCase :							Tuple								=				10_000
  UpperCAmelCase :							Any								=				timeit(f'''{name}(G, \'A\')'''       ,				setup=UpperCAmelCase       ,				number=UpperCAmelCase    )
  print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     benchmark_function("breadth_first_search")
     benchmark_function("breadth_first_search_with_deque")
     # breadth_first_search                finished 10000 runs in 0.20999 seconds
     # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





