

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def  lowerCamelCase__ (					_a							,			_a):
	assert isinstance(_a							,			_a)
	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__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      Union[str, 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():
		SCREAMING_SNAKE_CASE   :      List[str]				     =       JsonDatasetReader(_a							,			cache_dir=_a							,			keep_in_memory=_a).read()
	_check_json_dataset(_a							,			_a)

@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__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       features.copy() if features else default_expected_features
	SCREAMING_SNAKE_CASE   :      int				     =       (
	    Features({feature: Value(_a) for feature, dtype in features.items()}) if features is not None else None
	)
	SCREAMING_SNAKE_CASE   :      str				     =       JsonDatasetReader(_a							,			features=_a							,			cache_dir=_a).read()
	_check_json_dataset(_a							,			_a)

@pytest.mark.parametrize(
    "features"							,			[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]							,			)
def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {"col_3": "float64", "col_1": "string", "col_2": "int64"}
	SCREAMING_SNAKE_CASE   :      Tuple				     =       features.copy() if features else default_expected_features
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (
	    Features({feature: Value(_a) for feature, dtype in features.items()}) if features is not None else None
	)
	SCREAMING_SNAKE_CASE   :      int				     =       JsonDatasetReader(_a							,			features=_a							,			cache_dir=_a).read()
	assert isinstance(_a							,			_a)
	assert dataset.num_rows == 2
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_3", "col_1", "col_2"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype

def  lowerCamelCase__ (					_a							,			_a):
	# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
	SCREAMING_SNAKE_CASE   :      Dict				     =       {"col_2": "int64", "col_3": "float64", "col_1": "string"}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       features.copy()
	SCREAMING_SNAKE_CASE   :      str				     =       (
	    Features({feature: Value(_a) for feature, dtype in features.items()}) if features is not None else None
	)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      Tuple				     =       JsonDatasetReader(_a							,			features=_a							,			cache_dir=_a).read()
	assert isinstance(_a							,			_a)
	assert dataset.num_rows == 2
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_2", "col_3", "col_1"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("split"							,			[None, NamedSplit("train"), "train", "test"])
def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      Dict				     =       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	SCREAMING_SNAKE_CASE   :      Tuple				     =       JsonDatasetReader(_a							,			cache_dir=_a							,			split=_a).read()
	_check_json_dataset(_a							,			_a)
	assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"							,			[str, list])
def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if issubclass(_a							,			_a):
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       jsonl_path
	elif issubclass(_a							,			_a):
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [jsonl_path]
	SCREAMING_SNAKE_CASE   :      str				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      Any				     =       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	SCREAMING_SNAKE_CASE   :      str				     =       JsonDatasetReader(_a							,			cache_dir=_a).read()
	_check_json_dataset(_a							,			_a)

def  lowerCamelCase__ (					_a							,			_a							,			_a=("train",)):
	assert isinstance(_a							,			_a)
	for split in splits:
		SCREAMING_SNAKE_CASE   :      Union[str, 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  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      List[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():
		SCREAMING_SNAKE_CASE   :      Tuple				     =       JsonDatasetReader({"train": jsonl_path}							,			cache_dir=_a							,			keep_in_memory=_a).read()
	_check_json_datasetdict(_a							,			_a)

@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__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      int				     =       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       features.copy() if features else default_expected_features
	SCREAMING_SNAKE_CASE   :      int				     =       (
	    Features({feature: Value(_a) for feature, dtype in features.items()}) if features is not None else None
	)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       JsonDatasetReader({"train": jsonl_path}							,			features=_a							,			cache_dir=_a).read()
	_check_json_datasetdict(_a							,			_a)

@pytest.mark.parametrize("split"							,			[None, NamedSplit("train"), "train", "test"])
def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if split:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       {split: jsonl_path}
	else:
		SCREAMING_SNAKE_CASE   :      int				     =       "train"
		SCREAMING_SNAKE_CASE   :      int				     =       {"train": jsonl_path, "test": jsonl_path}
	SCREAMING_SNAKE_CASE   :      Dict				     =       tmp_path / "cache"
	SCREAMING_SNAKE_CASE   :      int				     =       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	SCREAMING_SNAKE_CASE   :      List[str]				     =       JsonDatasetReader(_a							,			cache_dir=_a).read()
	_check_json_datasetdict(_a							,			_a							,			splits=list(path.keys()))
	assert all(dataset[split].split == split for split in path.keys())

def  lowerCamelCase__ (					_a):
	return json.load(_a)

def  lowerCamelCase__ (					_a):
	return [json.loads(_a) for line in buffer]






class _UpperCamelCase							:
			'''simple docstring'''







			@pytest.mark.parametrize("lines, load_json_function"      ,      [(True, load_json_lines), (False, load_json)]	)
			def 							__UpperCamelCase (			self							: Any      ,      a							: Tuple      ,      a							: List[Any]      ,      a							: int	)							->       Dict:

				"""simple docstring"""

				with io.BytesIO() as buffer:
					JsonDatasetWriter(a      ,      a      ,      lines=a	).write()
					buffer.seek(0	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       load_json_function(a	)
				assert isinstance(a      ,      a	)
				assert isinstance(exported_content[0]      ,      a	)
				assert len(a	) == 10
			@pytest.mark.parametrize(
			    "orient, container, keys, len_at"      ,      [
			        ("records", list, {"tokens", "labels", "answers", "id"}, None),
			        ("split", dict, {"columns", "data"}, "data"),
			        ("index", dict, set("0123456789"	), None),
			        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
			        ("values", list, None, None),
			        ("table", dict, {"schema", "data"}, "data"),
			    ]      ,      )
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: List[Any]      ,      a							: Dict      ,      a							: Tuple      ,      a							: Any	)							->       str:

				"""simple docstring"""

				with io.BytesIO() as buffer:
					JsonDatasetWriter(a      ,      a      ,      lines=a      ,      orient=a	).write()
					buffer.seek(0	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       load_json(a	)
				assert isinstance(a      ,      a	)
				if keys:
					if container is dict:
						assert exported_content.keys() == keys
					else:
						assert exported_content[0].keys() == keys
				else:
					assert not hasattr(a      ,      "keys"	) and not hasattr(exported_content[0]      ,      "keys"	)
				if len_at:
					assert len(exported_content[len_at]	) == 10
				else:
					assert len(a	) == 10
			@pytest.mark.parametrize("lines, load_json_function"      ,      [(True, load_json_lines), (False, load_json)]	)
			def 							__UpperCamelCase (			self							: Any      ,      a							: Optional[Any]      ,      a							: List[Any]      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				with io.BytesIO() as buffer:
					JsonDatasetWriter(a      ,      a      ,      lines=a      ,      num_proc=2	).write()
					buffer.seek(0	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       load_json_function(a	)
				assert isinstance(a      ,      a	)
				assert isinstance(exported_content[0]      ,      a	)
				assert len(a	) == 10
			@pytest.mark.parametrize(
			    "orient, container, keys, len_at"      ,      [
			        ("records", list, {"tokens", "labels", "answers", "id"}, None),
			        ("split", dict, {"columns", "data"}, "data"),
			        ("index", dict, set("0123456789"	), None),
			        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
			        ("values", list, None, None),
			        ("table", dict, {"schema", "data"}, "data"),
			    ]      ,      )
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Optional[int]      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: List[str]      ,      a							: int	)							->       List[Any]:

				"""simple docstring"""

				with io.BytesIO() as buffer:
					JsonDatasetWriter(a      ,      a      ,      lines=a      ,      orient=a      ,      num_proc=2	).write()
					buffer.seek(0	)
					SCREAMING_SNAKE_CASE   :      Any				     =       load_json(a	)
				assert isinstance(a      ,      a	)
				if keys:
					if container is dict:
						assert exported_content.keys() == keys
					else:
						assert exported_content[0].keys() == keys
				else:
					assert not hasattr(a      ,      "keys"	) and not hasattr(exported_content[0]      ,      "keys"	)
				if len_at:
					assert len(exported_content[len_at]	) == 10
				else:
					assert len(a	) == 10
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: List[Any]	)							->       str:

				"""simple docstring"""

				with pytest.raises(a	):
					with io.BytesIO() as buffer:
						JsonDatasetWriter(a      ,      a      ,      num_proc=0	)






			@pytest.mark.parametrize("compression, extension"      ,      [("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]	)
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: List[str]      ,      a							: int      ,      a							: Optional[Any]      ,      a							: List[str]      ,      a							: str	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tmp_path_factory.mktemp("data"	) / F"test.json.{extension}"
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       str(shared_datadir / F"test_file.json.{extension}"	)
				JsonDatasetWriter(a      ,      a      ,      compression=a	).write()

				with fsspec.open(a      ,      "rb"      ,      compression="infer"	) as f:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       f.read()
				with fsspec.open(a      ,      "rb"      ,      compression="infer"	) as f:
					SCREAMING_SNAKE_CASE   :      Any				     =       f.read()
				assert exported_content == original_content


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)