'''simple docstring'''


import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_=False		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  Dict  = (
           filename_skip_take['filename'],
           filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
           filename_skip_take['take'] if 'take' in filename_skip_take else None,
       )
       open(os.path.join(snake_case_		) ,						'wb'		).close()
       UpperCAmelCase_				:  Tuple  = pa.Table.from_pydict({'filename': [Path(snake_case_		).name] * 1_0_0}		)
       if take == -1:
            UpperCAmelCase_				:  Optional[int]  = len(snake_case_		) - skip
       if skip is not None and take is not None:
            UpperCAmelCase_				:  Optional[int]  = pa_table.slice(snake_case_ ,						snake_case_		)
       return pa_table







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = 'my_name'
       UpperCAmelCase_				:  Dict  = SplitInfo(name='train' ,						num_examples=1_0_0		)
       UpperCAmelCase_				:  int  = SplitInfo(name='test' ,						num_examples=1_0_0		)
       UpperCAmelCase_				:  Any  = [train_info, test_info]
       UpperCAmelCase_				:  Union[str, Any]  = SplitDict()
       split_dict.add(snake_case_		)
       split_dict.add(snake_case_		)
       UpperCAmelCase_				:  List[str]  = DatasetInfo(splits=snake_case_		)

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Optional[int]  = ReaderTest(snake_case_ ,						snake_case_		)

            UpperCAmelCase_				:  Optional[int]  = 'test[:33%]'
            UpperCAmelCase_				:  Dict  = Dataset(**reader.read(snake_case_ ,						snake_case_ ,						snake_case_		)		)
            self.assertEqual(dset['filename'][0] ,						F'''{name}-test'''		)
            self.assertEqual(dset.num_rows ,						3_3		)
            self.assertEqual(dset.num_columns ,						1		)

            UpperCAmelCase_				:  List[str]  = ['train', 'test[:33%]']
            UpperCAmelCase_				:  Dict  = [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'		)]
            for instructions in [instructionsa, instructionsa]:
                 UpperCAmelCase_				:  Dict  = [reader.read(snake_case_ ,						snake_case_ ,						snake_case_		) for instr in instructions]
                 UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[Any]  = (Dataset(**snake_case_		) for dataset_kwargs in datasets_kwargs)
                 self.assertEqual(train_dset['filename'][0] ,						F'''{name}-train'''		)
                 self.assertEqual(train_dset.num_rows ,						1_0_0		)
                 self.assertEqual(train_dset.num_columns ,						1		)
                 self.assertIsInstance(train_dset.split ,						snake_case_		)
                 self.assertEqual(str(train_dset.split		) ,						'train'		)
                 self.assertEqual(test_dset['filename'][0] ,						F'''{name}-test'''		)
                 self.assertEqual(test_dset.num_rows ,						3_3		)
                 self.assertEqual(test_dset.num_columns ,						1		)
                 self.assertIsInstance(test_dset.split ,						snake_case_		)
                 self.assertEqual(str(test_dset.split		) ,						'test[:33%]'		)
                 del train_dset, test_dset


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = 'my_name'
       UpperCAmelCase_				:  Any  = SplitInfo(name='train' ,						num_examples=1_0_0_0 ,						shard_lengths=[1_0_0] * 1_0		)
       UpperCAmelCase_				:  Any  = [train_info]
       UpperCAmelCase_				:  Optional[int]  = SplitDict()
       split_dict.add(snake_case_		)
       UpperCAmelCase_				:  str  = DatasetInfo(splits=snake_case_		)

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Optional[Any]  = ReaderTest(snake_case_ ,						snake_case_		)

            UpperCAmelCase_				:  int  = 'train[:33%]'
            UpperCAmelCase_				:  Optional[Any]  = Dataset(**reader.read(snake_case_ ,						snake_case_ ,						snake_case_		)		)
            self.assertEqual(dset['filename'][0] ,						F'''{name}-train-00000-of-00010'''		)
            self.assertEqual(dset['filename'][-1] ,						F'''{name}-train-00003-of-00010'''		)
            self.assertEqual(dset.num_rows ,						3_3_0		)
            self.assertEqual(dset.num_columns ,						1		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = SplitInfo(name='train' ,						num_examples=1_0_0		)
       UpperCAmelCase_				:  Tuple  = SplitInfo(name='test' ,						num_examples=1_0_0		)
       UpperCAmelCase_				:  str  = SplitDict()
       split_dict.add(snake_case_		)
       split_dict.add(snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = DatasetInfo(splits=snake_case_		)

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Dict  = ReaderTest(snake_case_ ,						snake_case_		)

            UpperCAmelCase_				:  Optional[Any]  = [
                {'filename': os.path.join(snake_case_ ,						'train'		)},
                {'filename': os.path.join(snake_case_ ,						'test'		), 'skip': 1_0, 'take': 1_0},
            ]
            UpperCAmelCase_				:  Optional[Any]  = Dataset(**reader.read_files(snake_case_ ,						original_instructions='train+test[10:20]'		)		)
            self.assertEqual(dset.num_rows ,						1_1_0		)
            self.assertEqual(dset.num_columns ,						1		)
            del dset







@pytest.mark.parametrize('in_memory'      ,	[False, True]  )
def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          UpperCAmelCase_				:  str  = ArrowReader.read_table(lowerCamelCase_      ,	in_memory=lowerCamelCase_  )
     assert table.shape == dataset.data.shape
     assert set(table.column_names  ) == set(dataset.data.column_names  )
     assert dict(table.to_pydict()  ) == dict(dataset.data.to_pydict()  )  # to_pydict returns OrderedDict


@pytest.mark.parametrize('in_memory'      ,	[False, True]  )
def    _lowerCamelCase       ( lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				List[Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = arrow_file
     UpperCAmelCase_				:  Dict  = ArrowReader(''      ,	lowerCamelCase_  )
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          UpperCAmelCase_				:  List[str]  = reader.read_files([{'filename': filename}]      ,	in_memory=lowerCamelCase_  )
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     UpperCAmelCase_				:  Union[str, Any]  = dataset_kwargs['arrow_table']
     assert table.shape == dataset.data.shape
     assert set(table.column_names  ) == set(dataset.data.column_names  )
     assert dict(table.to_pydict()  ) == dict(dataset.data.to_pydict()  )  # to_pydict returns OrderedDict


def    _lowerCamelCase       ( ):



     """simple docstring"""





     assert ReadInstruction('train'      ,	to=10      ,	unit='abs'  ).to_spec() == "train[:10]"
     assert ReadInstruction('train'      ,	from_=-80      ,	to=10      ,	unit='%'  ).to_spec() == "train[-80%:10%]"

     UpperCAmelCase_				:  Dict  = 'train+test'
     assert ReadInstruction.from_spec(lowerCamelCase_  ).to_spec() == spec_train_test

     UpperCAmelCase_				:  List[Any]  = 'train[2:10]'
     assert ReadInstruction.from_spec(lowerCamelCase_  ).to_spec() == spec_train_abs

     UpperCAmelCase_				:  Any  = 'train[15%:-20%]'
     assert ReadInstruction.from_spec(lowerCamelCase_  ).to_spec() == spec_train_pct

     UpperCAmelCase_				:  Dict  = 'train[:10%](closest)'
     assert ReadInstruction.from_spec(lowerCamelCase_  ).to_spec() == "train[:10%]"

     UpperCAmelCase_				:  Tuple  = 'train[:10%](pct1_dropremainder)'
     assert ReadInstruction.from_spec(lowerCamelCase_  ).to_spec() == spec_train_pct_rounding

     UpperCAmelCase_				:  Union[str, Any]  = 'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
     assert ReadInstruction.from_spec(lowerCamelCase_  ).to_spec() == spec_train_test_pct_rounding


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = 'dummy'
     UpperCAmelCase_				:  List[Any]  = [SplitInfo(name='train'      ,	num_examples=100  )]
     UpperCAmelCase_				:  Union[str, Any]  = 'train[:33%]'
     UpperCAmelCase_				:  Any  = 'arrow'
     UpperCAmelCase_				:  Optional[Any]  = 'prefix'

     UpperCAmelCase_				:  Union[str, Any]  = make_file_instructions(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )
     assert isinstance(lowerCamelCase_      ,	lowerCamelCase_  )
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(lowerCamelCase_      ,	F'''{name}-train.arrow'''  ), "skip": 0, "take": 33}
     ]

     UpperCAmelCase_				:  Optional[int]  = [SplitInfo(name='train'      ,	num_examples=100      ,	shard_lengths=[10] * 10  )]
     UpperCAmelCase_				:  Optional[Any]  = make_file_instructions(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )
     assert isinstance(lowerCamelCase_      ,	lowerCamelCase_  )
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(lowerCamelCase_      ,	F'''{name}-train-00000-of-00010.arrow'''  ), "skip": 0, "take": -1},
         {"filename": os.path.join(lowerCamelCase_      ,	F'''{name}-train-00001-of-00010.arrow'''  ), "skip": 0, "take": -1},
         {"filename": os.path.join(lowerCamelCase_      ,	F'''{name}-train-00002-of-00010.arrow'''  ), "skip": 0, "take": -1},
         {"filename": os.path.join(lowerCamelCase_      ,	F'''{name}-train-00003-of-00010.arrow'''  ), "skip": 0, "take": 3},
     ]





@pytest.mark.parametrize('name, expected_exception'      ,	[(None, TypeError), ('', ValueError)]  )
def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = [SplitInfo(name='train'      ,	num_examples=100  )]
     UpperCAmelCase_				:  List[Any]  = 'train'
     UpperCAmelCase_				:  Optional[int]  = 'arrow'
     UpperCAmelCase_				:  int  = 'prefix_path'
     with pytest.raises(lowerCamelCase_  ):
          UpperCAmelCase_				:  Any  = make_file_instructions(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )







'''simple docstring'''


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = key
       UpperCAmelCase_				:  Tuple  = freq






  def __str__(					self		):




       '''simple docstring'''

       return F'''Node(key={self.key}, freq={self.freq})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     if i > j or i < 0 or j > len(lowerCamelCase_  ) - 1:
          return

     UpperCAmelCase_				:  Optional[int]  = root[i][j]
     if parent == -1:  # root does not have a parent
          print(F'''{key[node]} is the root of the binary search tree.'''  )
     elif is_left:
          print(F'''{key[node]} is the left child of key {parent}.'''  )
     else:
          print(F'''{key[node]} is the right child of key {parent}.'''  )

     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	node - 1      ,	key[node]      ,	lowerCamelCase_  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	node + 1      ,	lowerCamelCase_      ,	key[node]      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     nodes.sort(key=lambda lowerCamelCase_  : node.key  )

     UpperCAmelCase_				:  int  = len(lowerCamelCase_  )

     UpperCAmelCase_				:  Dict  = [nodes[i].key for i in range(lowerCamelCase_  )]
     UpperCAmelCase_				:  Any  = [nodes[i].freq for i in range(lowerCamelCase_  )]

     # This 2D array stores the overall tree cost (which's as minimized as possible);
     # for a single key, cost is equal to frequency of the key.
     UpperCAmelCase_				:  Optional[Any]  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     UpperCAmelCase_				:  Any  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # stores tree roots that will be used later for constructing binary search tree
     UpperCAmelCase_				:  Optional[int]  = [[i if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]

     for interval_length in range(2      ,	n + 1  ):
          for i in range(n - interval_length + 1  ):
               UpperCAmelCase_				:  Optional[Any]  = i + interval_length - 1

               UpperCAmelCase_				:  Tuple  = sys.maxsize  # set the value to "infinity"
               UpperCAmelCase_				:  Any  = total[i][j - 1] + freqs[j]

               # Apply Knuth's optimization
               # Loop without optimization: for r in range(i, j + 1):
               for r in range(root[i][j - 1]      ,	root[i + 1][j] + 1  ):  # r is a temporal root
                    UpperCAmelCase_				:  str  = dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                    UpperCAmelCase_				:  Optional[int]  = dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    UpperCAmelCase_				:  Optional[int]  = left + total[i][j] + right

                    if dp[i][j] > cost:
                         UpperCAmelCase_				:  Dict  = cost
                         UpperCAmelCase_				:  Tuple  = r

     print('Binary search tree nodes:'  )
     for node in nodes:
          print(lowerCamelCase_  )

     print(F'''\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'''  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	0      ,	n - 1      ,	-1      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = [Node(lowerCamelCase_      ,	randint(1      ,	50  )  ) for i in range(10      ,	0      ,	-1  )]
     find_optimal_binary_search_tree(lowerCamelCase_  )


if __name__ == "__main__":
 main()






