





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


_A					:				List[Any]				=			datasets.utils.logging.get_logger(__name__)



@dataclass
class 					__snake_case	(  datasets.BuilderConfig    ):


    '''simple docstring'''







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





class 					__snake_case	(  datasets.ArrowBasedBuilder    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	JsonConfig



    def  lowercase_					(       self     ):





        '''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  lowercase_					(       self   ,				A_     ):





        '''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(A_   ,				(str, list, tuple)     ):
            SCREAMING_SNAKE_CASE__											=			data_files
            if isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			[files]
            SCREAMING_SNAKE_CASE__											=			[dl_manager.iter_files(A_     ) 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(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			[files]
            SCREAMING_SNAKE_CASE__											=			[dl_manager.iter_files(A_     ) for file in files]
            splits.append(datasets.SplitGenerator(name=A_   ,				gen_kwargs={'''files''': files}     )     )
        return splits



    def  lowercase_					(       self   ,				A_     ):





        '''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(A_     ).type
                SCREAMING_SNAKE_CASE__											=			pa_table.append_column(A_   ,				pa.array([None] * len(A_     )   ,				type=A_     )     )
            # 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(A_   ,				self.config.features.arrow_schema     )
        return pa_table




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

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

                # 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(A_   ,				(list, tuple)     ):
                    SCREAMING_SNAKE_CASE__											=			set().union(*[row.keys() for row in dataset]     )
                    SCREAMING_SNAKE_CASE__											=			{col: [row.get(A_     ) for row in dataset] for col in keys}
                else:
                    SCREAMING_SNAKE_CASE__											=			dataset
                SCREAMING_SNAKE_CASE__											=			pa.Table.from_pydict(A_     )
                yield file_idx, self._cast_table(A_     )

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








'''simple docstring'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




