





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowercase :			Any     		=    datasets.utils.logging.get_logger(__name__)





@dataclass
class 					A__       (				datasets.BuilderConfig      ):




   """simple docstring"""



   __A  :							Optional[datasets.Features]			    =    None
   __A  :							str			    =    "utf-8"
   __A  :							Optional[str]			    =    None
   __A  :							Optional[str]			    =    None
   __A  :							bool			    =    True  # deprecated
   __A  :							Optional[int]			    =    None  # deprecated
   __A  :							int			    =    1_0 << 2_0  # 10MB
   __A  :							Optional[bool]			    =    None





class 					A__       (				datasets.ArrowBasedBuilder      ):




   """simple docstring"""



   __A  :							Any			    =    JsonConfig

   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          if self.config.block_size is not None:
                 logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead')
                 a__		:      Dict					=							self.config.block_size
          if self.config.use_threads is not True:
                 logger.warning(
                     'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.')
          if self.config.newlines_in_values is not None:
                 raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported')
          return datasets.DatasetInfo(features=self.config.features)

   def 				__lowercase						(   self   ,    lowercase)     ->		Union[str, Any]:







          '''simple docstring'''

          if not self.config.data_files:
                 raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
          a__		:      Optional[Any]					=							dl_manager.download_and_extract(self.config.data_files)
          if isinstance(SCREAMING_SNAKE_CASE_   ,    (str, list, tuple)):
                 a__		:      Dict					=							data_files
                 if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_):
                        a__		:      Any					=							[files]
                 a__		:      str					=							[dl_manager.iter_files(SCREAMING_SNAKE_CASE_) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs={'files': files})]
          a__		:      str					=							[]
          for split_name, files in data_files.items():
                 if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_):
                        a__		:      str					=							[files]
                 a__		:      List[Any]					=							[dl_manager.iter_files(SCREAMING_SNAKE_CASE_) for file in files]
                 splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_   ,    gen_kwargs={'files': files}))
          return splits

   def 				__lowercase						(   self   ,    lowercase)     ->		pa.Table:







          '''simple docstring'''

          if self.config.features is not None:
                 # adding missing columns
                 for column_name in set(self.config.features) - set(pa_table.column_names):
                        a__		:      Optional[int]					=							self.config.features.arrow_schema.field(SCREAMING_SNAKE_CASE_).type
                        a__		:      List[str]					=							pa_table.append_column(SCREAMING_SNAKE_CASE_   ,    pa.array([None] * len(SCREAMING_SNAKE_CASE_)   ,    type=SCREAMING_SNAKE_CASE_))
                 # more expensive cast to support nested structures with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 a__		:      Dict					=							table_cast(SCREAMING_SNAKE_CASE_   ,    self.config.features.arrow_schema)
          return pa_table

   def 				__lowercase						(   self   ,    lowercase)     ->		List[str]:







          '''simple docstring'''

          for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_)):
                 # If the file is one json object and if we need to look at the list of items in one specific field
                 if self.config.field is not None:
                        with open(SCREAMING_SNAKE_CASE_   ,    encoding=self.config.encoding   ,    errors=self.config.encoding_errors) as f:
                               a__		:      List[Any]					=							json.load(SCREAMING_SNAKE_CASE_)

                        # We keep only the field we are interested in
                        a__		:      Optional[Any]					=							dataset[self.config.field]

                        # We accept two format: a list of dicts or a dict of lists
                        if isinstance(SCREAMING_SNAKE_CASE_   ,    (list, tuple)):
                               a__		:      str					=							set().union(*[row.keys() for row in dataset])
                               a__		:      Dict					=							{col: [row.get(SCREAMING_SNAKE_CASE_) for row in dataset] for col in keys}
                        else:
                               a__		:      Union[str, Any]					=							dataset
                        a__		:      Dict					=							pa.Table.from_pydict(SCREAMING_SNAKE_CASE_)
                        yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE_)

                 # If the file has one json object per line
                 else:
                        with open(SCREAMING_SNAKE_CASE_   ,    'rb') as f:
                               a__		:      List[Any]					=							0
                               # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                               # Set a default minimum value of 16kB if the chunk size is really small
                               a__		:      Optional[int]					=							max(self.config.chunksize // 32   ,    16 << 10)
                               a__		:      List[Any]					=							(
                                   self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
                               )
                               while True:
                                      a__		:      List[Any]					=							f.read(self.config.chunksize)
                                      if not batch:
                                             break
                                      # Finish current line
                                      try:
                                             batch += f.readline()
                                      except (AttributeError, io.UnsupportedOperation):
                                             batch += readline(SCREAMING_SNAKE_CASE_)
                                      # PyArrow only accepts utf-8 encoded bytes
                                      if self.config.encoding != "utf-8":
                                             a__		:      str					=							batch.decode(self.config.encoding   ,    errors=SCREAMING_SNAKE_CASE_).encode('utf-8')
                                      try:
                                             while True:
                                                    try:
                                                           a__		:      List[str]					=							paj.read_json(
                                                               io.BytesIO(SCREAMING_SNAKE_CASE_)   ,    read_options=paj.ReadOptions(block_size=SCREAMING_SNAKE_CASE_))
                                                           break
                                                    except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                           if (
                                                               isinstance(SCREAMING_SNAKE_CASE_   ,    pa.ArrowInvalid)
                                                               and "straddling" not in str(SCREAMING_SNAKE_CASE_)
                                                               or block_size > len(SCREAMING_SNAKE_CASE_)
                                                           ):
                                                                  raise
                                                           else:
                                                                  # Increase the block size in case it was too small.
                                                                  # The block size will be reset for the next file.
                                                                  logger.debug(
                                                                      F'Batch of {len(SCREAMING_SNAKE_CASE_)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.')
                                                                  block_size *= 2
                                      except pa.ArrowInvalid as e:
                                             try:
                                                    with open(
                                                        SCREAMING_SNAKE_CASE_   ,    encoding=self.config.encoding   ,    errors=self.config.encoding_errors) as f:
                                                           a__		:      Optional[int]					=							json.load(SCREAMING_SNAKE_CASE_)
                                             except json.JSONDecodeError:
                                                    logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_)}: {e}')
                                                    raise e
                                             # If possible, parse the file as a list of json objects and exit the loop
                                             if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_):  # list is the only sequence type supported in JSON
                                                    try:
                                                           a__		:      Optional[Any]					=							set().union(*[row.keys() for row in dataset])
                                                           a__		:      str					=							{col: [row.get(SCREAMING_SNAKE_CASE_) for row in dataset] for col in keys}
                                                           a__		:      Tuple					=							pa.Table.from_pydict(SCREAMING_SNAKE_CASE_)
                                                    except (pa.ArrowInvalid, AttributeError) as e:
                                                           logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_)}: {e}')
                                                           raise ValueError(F'Not able to read records in the JSON file at {file}.') from None
                                                    yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE_)
                                                    break
                                             else:
                                                    logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_)}: {e}')
                                                    raise ValueError(
                                                        F'Not able to read records in the JSON file at {file}. '
                                                        F'You should probably indicate the field of the JSON file containing your records. '
                                                        F'This JSON file contain the following fields: {str(list(dataset.keys()))}. '
                                                        F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ') from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                      yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE_)
                                      batch_idx += 1




import unittest

from knapsack import knapsack as k
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   0
					lowercase_	    =   [0]
					lowercase_	    =   [0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)

					lowercase_	    =   [6_0]
					lowercase_	    =   [1_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)
	def 		_lowercase    (			self    :     int			)			->	str:
					lowercase_	    =   3
					lowercase_	    =   [1, 2, 3]
					lowercase_	    =   [3, 2, 1]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				5			)




	def 		_lowercase    (			self    :     str			)			->	Tuple:
					lowercase_	    =   5_0
					lowercase_	    =   [6_0, 1_0_0, 1_2_0]
					lowercase_	    =   [1_0, 2_0, 3_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				2_2_0			)


if __name__ == "__main__":
						unittest.main()




