
'''simple docstring'''

from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize






class    A     (	SCREAMING_SNAKE_CASE__    ):
					snake_case__      :str        =     1
					snake_case__      :Union[str, Any]        =     2
					snake_case__      :Union[str, Any]        =     3





					def __repr__(					self			:       Union[str, Any]							):


								"""simple docstring"""

								return f"""{self.__class__.__name__}.{self.name}"""







def   A     (      UpperCamelCase_					:     tuple[int, int]						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int      )					->       Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__								=							point
			return degrees(atana(y - miny						,	x - minx      )      )







def   A     (      UpperCamelCase_					:     tuple[int, int]						,	UpperCamelCase_					:     tuple[int, int]						,	UpperCamelCase_					:     tuple[int, int]      )					->       Dict:







			'''simple docstring'''



			lowerCAmelCase__								=							starting
			lowerCAmelCase__								=							via
			lowerCAmelCase__								=							target
			lowerCAmelCase__								=							degrees(atana(ya - ya						,	xa - xa      )      )
			via_angle %= 3_60
			lowerCAmelCase__								=							degrees(atana(ya - ya						,	xa - xa      )      )
			target_angle %= 3_60
			# t-
			#  \ \
			#   \ v
			#    \|
			#     s
			# via_angle is always lower than target_angle, if direction is left.
			# If they are same, it means they are on a same line of convex hull.
			if target_angle > via_angle:
						return Direction.left
			elif target_angle == via_angle:
						return Direction.straight
			else:
						return Direction.right







def   A     (      UpperCamelCase_					:     list[tuple[int, int]]      )					->       str:







			'''simple docstring'''



			if len(UpperCamelCase__      ) <= 2:
						# There is no convex hull
						raise ValueError("graham_scan: argument must contain more than 3 points."      )
			if len(UpperCamelCase__      ) == 3:
						return points
			# find the lowest and the most left point
			lowerCAmelCase__								=							0
			lowerCAmelCase__								=							maxsize, maxsize
			for i, point in enumerate(UpperCamelCase__      ):
						lowerCAmelCase__								=							point[0]
						lowerCAmelCase__								=							point[1]
						if y < miny:
									lowerCAmelCase__								=							y
									lowerCAmelCase__								=							x
									lowerCAmelCase__								=							i
						if y == miny and x < minx:
									lowerCAmelCase__								=							x
									lowerCAmelCase__								=							i

    # remove the lowest and the most left point from points for preparing for sort
			points.pop(UpperCamelCase__      )

			lowerCAmelCase__								=							sorted(UpperCamelCase__						,	key=lambda UpperCamelCase_      : angle_comparer(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )      )
			# This insert actually costs complexity,
			# and you should instead add (minx, miny) into stack later.
			# I'm using insert just for easy understanding.
			sorted_points.insert(0						,	(minx, miny)      )

			lowerCAmelCase__								=							deque()
			stack.append(sorted_points[0]      )
			stack.append(sorted_points[1]      )
			stack.append(sorted_points[2]      )
			# In any ways, the first 3 points line are towards left.
			# Because we sort them the angle from minx, miny.
			lowerCAmelCase__								=							Direction.left

			for i in range(3						,	len(UpperCamelCase__      )      ):
						while True:
									lowerCAmelCase__								=							stack[-2]
									lowerCAmelCase__								=							stack[-1]
									lowerCAmelCase__								=							sorted_points[i]
									lowerCAmelCase__								=							check_direction(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__      )

									if next_direction == Direction.left:
												lowerCAmelCase__								=							Direction.left
												break
									if next_direction == Direction.straight:
												if current_direction == Direction.left:
															# We keep current_direction as left.
															# Because if the straight line keeps as straight,
															# we want to know if this straight line is towards left.
															break
												elif current_direction == Direction.right:
															# If the straight line is towards right,
															# every previous points on those straigh line is not convex hull.
															stack.pop()
									if next_direction == Direction.right:
												stack.pop()
						stack.append(sorted_points[i]      )
			return list(UpperCamelCase__      )







'''simple docstring'''

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_					:     List[str]						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



			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_					:     Optional[int]						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							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_					:     List[Any]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							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_					:     str						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     str      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							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_					:     Any						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     List[str]      )					->       Optional[int]:







			'''simple docstring'''



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







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     List[str]=("train",)      )					->       Optional[int]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			for split in splits:
						lowerCAmelCase__								=							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_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Optional[int]      )					->       Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							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_					:     str						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Dict      )					->       Optional[int]:







			'''simple docstring'''



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







			'''simple docstring'''



			if split:
						lowerCAmelCase__								=							{split: csv_path}
			else:
						lowerCAmelCase__								=							{"train": csv_path, "test": csv_path}
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							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_					:     str      )					->       List[str]:







			'''simple docstring'''



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







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



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

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Dict      )					->       List[str]:







			'''simple docstring'''



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

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



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





