

from __future__ import annotations

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

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Union[str, Any]					=					1
      _lowerCamelCase					: Union[str, Any]					=					2
      _lowerCamelCase					: Optional[Any]					=					3





      def __repr__(      self       :							Optional[Any]  ):
            return f'''{self.__class__.__name__}.{self.name}'''




def 			__snake_case	(   __UpperCamelCase  :   tuple[int, int]							,__UpperCamelCase  :   int							,__UpperCamelCase  :   int						):



      """simple docstring"""

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

def 			__snake_case	(   __UpperCamelCase  :   tuple[int, int]							,__UpperCamelCase  :   tuple[int, int]							,__UpperCamelCase  :   tuple[int, int]						):



      """simple docstring"""

      A_     ,    A_	     =							starting
      A_     ,    A_	     =							via
      A_     ,    A_	     =							target
      A_	     =							degrees(atana(ya - ya							,xa - xa						)						)
      via_angle %= 360
      A_	     =							degrees(atana(ya - ya							,xa - xa						)						)
      target_angle %= 360
      # 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 			__snake_case	(   __UpperCamelCase  :   list[tuple[int, int]]						):



      """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
      A_	     =							0
      A_     ,    A_	     =							maxsize, maxsize
      for i, point in enumerate(__UpperCamelCase						):
            A_	     =							point[0]
            A_	     =							point[1]
            if y < miny:
                  A_	     =							y
                  A_	     =							x
                  A_	     =							i
            if y == miny and x < minx:
                  A_	     =							x
                  A_	     =							i

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

      A_	     =							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)						)

      A_	     =							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.
      A_	     =							Direction.left

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

                  if next_direction == Direction.left:
                        A_	     =							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						)


import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with open(__UpperCamelCase							,"wb"						) as f:
            f.write(json.dumps(__UpperCamelCase						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()