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


__lowerCamelCase    :				Dict							     =	datasets.utils.logging.get_logger(__name__)







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							"utf-8"
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							True  # deprecated
   lowerCAmelCase_														=							None  # deprecated
   lowerCAmelCase_														=							10 << 20  # 10MB
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							JsonConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     if self.config.block_size is not None:
       logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""   )
       SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       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}"""   )
     SCREAMING_SNAKE_CASE__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_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   ):
         SCREAMING_SNAKE_CASE__    							=   self.config.features.arrow_schema.field(_lowercase   ).type
         SCREAMING_SNAKE_CASE__    							=   pa_table.append_column(_lowercase ,			pa.array([None] * len(_lowercase   ) ,			type=_lowercase   )   )
       # more expensive cast to support nested structures with keys in a different order
       # allows str <-> int/float or str to Audio for example
       SCREAMING_SNAKE_CASE__    							=   table_cast(_lowercase ,			self.config.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       str   ):



     """simple docstring"""






     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase   )   ):
       # 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(_lowercase ,			encoding=self.config.encoding ,			errors=self.config.encoding_errors   ) as f:
           SCREAMING_SNAKE_CASE__    							=   json.load(_lowercase   )

         # We keep only the field we are interested in
         SCREAMING_SNAKE_CASE__    							=   dataset[self.config.field]

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

       # If the file has one json object per line
       else:
         with open(_lowercase ,			"""rb"""   ) as f:
           SCREAMING_SNAKE_CASE__    							=   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
           SCREAMING_SNAKE_CASE__    							=   max(self.config.chunksize // 32 ,			16 << 10   )
           SCREAMING_SNAKE_CASE__    							=   (
               self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
           )
           while True:
             SCREAMING_SNAKE_CASE__    							=   f.read(self.config.chunksize   )
             if not batch:
               break
             # Finish current line
             try:
               batch += f.readline()
             except (AttributeError, io.UnsupportedOperation):
               batch += readline(_lowercase   )
             # PyArrow only accepts utf-8 encoded bytes
             if self.config.encoding != "utf-8":
               SCREAMING_SNAKE_CASE__    							=   batch.decode(self.config.encoding ,			errors=_lowercase   ).encode("""utf-8"""   )
             try:
               while True:
                 try:
                   SCREAMING_SNAKE_CASE__    							=   paj.read_json(
                       io.BytesIO(_lowercase   ) ,			read_options=paj.ReadOptions(block_size=_lowercase   )   )
                   break
                 except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                   if (
                       isinstance(_lowercase ,			pa.ArrowInvalid   )
                       and "straddling" not in str(_lowercase   )
                       or block_size > len(_lowercase   )
                   ):
                     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(_lowercase   )} 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(
                     _lowercase ,			encoding=self.config.encoding ,			errors=self.config.encoding_errors   ) as f:
                   SCREAMING_SNAKE_CASE__    							=   json.load(_lowercase   )
               except json.JSONDecodeError:
                 logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
                 raise e
               # If possible, parse the file as a list of json objects and exit the loop
               if isinstance(_lowercase ,			_lowercase   ):  # list is the only sequence type supported in JSON
                 try:
                   SCREAMING_SNAKE_CASE__    							=   set().union(*[row.keys() for row in dataset]   )
                   SCREAMING_SNAKE_CASE__    							=   {col: [row.get(_lowercase   ) for row in dataset] for col in keys}
                   SCREAMING_SNAKE_CASE__    							=   pa.Table.from_pydict(_lowercase   )
                 except (pa.ArrowInvalid, AttributeError) as e:
                   logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
                   raise ValueError(f"""Not able to read records in the JSON file at {file}."""   ) from None
                 yield file_idx, self._cast_table(_lowercase   )
                 break
               else:
                 logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {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(_lowercase   )
             batch_idx += 1

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  if index == number_of_items:
    return 0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   knapsack(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  index + 1			)
  if weights[index] <= max_weight:
    SCREAMING_SNAKE_CASE__    							=   values[index] + knapsack(
        __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  max_weight - weights[index]				,  index + 1			)
  return max(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
