




import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				assert isinstance(lowerCamelCase						,  lowerCamelCase )
				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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Dict										=tmp_path / """cache"""
				__magic_name__						:						int										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__						:						Dict										=ParquetDatasetReader(lowerCamelCase						,  cache_dir=lowerCamelCase						,  keep_in_memory=lowerCamelCase ).read()
				_check_parquet_dataset(lowerCamelCase						,  lowerCamelCase )




@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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=tmp_path / """cache"""
				__magic_name__						:						Dict										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						List[str]										=features.copy() if features else default_expected_features
				__magic_name__						:						Any										=(
				    Features({feature: Value(lowerCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				__magic_name__						:						Dict										=ParquetDatasetReader(lowerCamelCase						,  features=lowerCamelCase						,  cache_dir=lowerCamelCase ).read()
				_check_parquet_dataset(lowerCamelCase						,  lowerCamelCase )




@pytest.mark.parametrize("""split"""						,  [None, NamedSplit("""train""" ), """train""", """test"""] )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=tmp_path / """cache"""
				__magic_name__						:						List[Any]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						Optional[Any]										=ParquetDatasetReader(lowerCamelCase						,  cache_dir=lowerCamelCase						,  split=lowerCamelCase ).read()
				_check_parquet_dataset(lowerCamelCase						,  lowerCamelCase )
				assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""						,  [str, list] )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				if issubclass(lowerCamelCase						,  lowerCamelCase ):
								__magic_name__						:						Dict										=parquet_path
				elif issubclass(lowerCamelCase						,  lowerCamelCase ):
								__magic_name__						:						str										=[parquet_path]
				__magic_name__						:						str										=tmp_path / """cache"""
				__magic_name__						:						Tuple										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						List[str]										=ParquetDatasetReader(lowerCamelCase						,  cache_dir=lowerCamelCase ).read()
				_check_parquet_dataset(lowerCamelCase						,  lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=("train",) ):
				assert isinstance(lowerCamelCase						,  lowerCamelCase )
				for split in splits:
								__magic_name__						:						Tuple										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[int]										=tmp_path / """cache"""
				__magic_name__						:						Any										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__						:						str										=ParquetDatasetReader(
								    {"""train""": parquet_path}						,  cache_dir=lowerCamelCase						,  keep_in_memory=lowerCamelCase ).read()
				_check_parquet_datasetdict(lowerCamelCase						,  lowerCamelCase )




@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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[int]										=tmp_path / """cache"""
				__magic_name__						:						List[Any]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						List[str]										=features.copy() if features else default_expected_features
				__magic_name__						:						Optional[Any]										=(
				    Features({feature: Value(lowerCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				__magic_name__						:						Union[str, Any]										=ParquetDatasetReader({"""train""": parquet_path}						,  features=lowerCamelCase						,  cache_dir=lowerCamelCase ).read()
				_check_parquet_datasetdict(lowerCamelCase						,  lowerCamelCase )




@pytest.mark.parametrize("""split"""						,  [None, NamedSplit("""train""" ), """train""", """test"""] )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				if split:
								__magic_name__						:						Optional[int]										={split: parquet_path}
				else:
								__magic_name__						:						int										="""train"""
								__magic_name__						:						Dict										={"""train""": parquet_path, """test""": parquet_path}
				__magic_name__						:						Dict										=tmp_path / """cache"""
				__magic_name__						:						Optional[Any]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						List[str]										=ParquetDatasetReader(lowerCamelCase						,  cache_dir=lowerCamelCase ).read()
				_check_parquet_datasetdict(lowerCamelCase						,  lowerCamelCase						,  splits=list(path.keys() ) )
				assert all(dataset[split].split == split for split in path.keys() )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=ParquetDatasetWriter(lowerCamelCase						,  tmp_path / """foo.parquet""" )
				assert writer.write() > 0
				__magic_name__						:						Any										=pq.ParquetFile(tmp_path / """foo.parquet""" )
				__magic_name__						:						Optional[int]										=pf.read()
				assert dataset.data.table == output_table




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=str(shared_datadir / """test_image_rgb.jpg""" )
				__magic_name__						:						Tuple										={"""image""": [image_path]}
				__magic_name__						:						str										=Features({"""image""": Image()} )
				__magic_name__						:						Any										=Dataset.from_dict(lowerCamelCase						,  features=lowerCamelCase )
				__magic_name__						:						int										=ParquetDatasetWriter(lowerCamelCase						,  tmp_path / """foo.parquet""" )
				assert writer.write() > 0

				__magic_name__						:						List[str]										=Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
				assert dataset.features == reloaded_dataset.features

				__magic_name__						:						Any										=ParquetDatasetReader(str(tmp_path / """foo.parquet""" )						,  streaming=lowerCamelCase ).read()
				assert dataset.features == reloaded_iterable_dataset.features




@pytest.mark.parametrize(
    """feature, expected"""						,  [
        (Features({"""foo""": Value("""int32""" )} ), None),
        (Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]						,  )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				assert get_writer_batch_size(lowerCamelCase ) == expected










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




