"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Dict:
    _lowerCamelCase	: Tuple       =       np.inf

    def set_batch_size(SCREAMING_SNAKE_CASE_ ) -> None:
        nonlocal batch_size
        if isinstance(lowerCamelCase_						,	lowerCamelCase_ ):
            _lowerCamelCase	: Optional[int]       =       min(lowerCamelCase_						,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
        elif isinstance(lowerCamelCase_						,	lowerCamelCase_ ):
            _lowerCamelCase	: List[str]       =       min(lowerCamelCase_						,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
        elif isinstance(lowerCamelCase_						,	lowerCamelCase_ ) and feature.dtype == "binary":
            _lowerCamelCase	: List[str]       =       min(lowerCamelCase_						,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )

    _visit(lowerCamelCase_						,	lowerCamelCase_ )

    return None if batch_size is np.inf else batch_size



class      _UpperCAmelCase						( A__						):






    """simple docstring"""







    def __init__(			self    , _lowercase    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = False    , _lowercase = False    , _lowercase = None    , **_lowercase    , )	->	List[Any]:
        super().__init__(
            _lowercase    , split=_lowercase    , features=_lowercase    , cache_dir=_lowercase    , keep_in_memory=_lowercase    , streaming=_lowercase    , num_proc=_lowercase    , **_lowercase    , )
        _lowerCamelCase	: Optional[Any]       =       path_or_paths if isinstance(_lowercase    , _lowercase					) else {self.split: path_or_paths}
        _lowerCamelCase	: Tuple       =       _PACKAGED_DATASETS_MODULES['''parquet'''][1]
        _lowerCamelCase	: Tuple       =       Parquet(
            cache_dir=_lowercase    , data_files=_lowercase    , features=_lowercase    , hash=_lowercase    , **_lowercase    , )







    def 				a__					(			self					)	->	Any:
        # Build iterable dataset
        if self.streaming:
            _lowerCamelCase	: Tuple       =       self.builder.as_streaming_dataset(split=self.split					)
        # Build regular (map-style) dataset
        else:
            _lowerCamelCase	: Union[str, Any]       =       None
            _lowerCamelCase	: List[Any]       =       None
            _lowerCamelCase	: List[Any]       =       None
            _lowerCamelCase	: str       =       None

            self.builder.download_and_prepare(
                download_config=_lowercase    , download_mode=_lowercase    , verification_mode=_lowercase    , base_path=_lowercase    , num_proc=self.num_proc    , )
            _lowerCamelCase	: List[Any]       =       self.builder.as_dataset(
                split=self.split    , verification_mode=_lowercase    , in_memory=self.keep_in_memory					)
        return dataset





class      _UpperCAmelCase						:






    """simple docstring"""







    def __init__(			self    , _lowercase    , _lowercase    , _lowercase = None    , **_lowercase    , )	->	Any:
        _lowerCamelCase	: List[Any]       =       dataset
        _lowerCamelCase	: Union[str, Any]       =       path_or_buf
        _lowerCamelCase	: Any       =       batch_size or get_writer_batch_size(dataset.features					)
        _lowerCamelCase	: Tuple       =       parquet_writer_kwargs







    def 				a__					(			self					)	->	Optional[Any]:
        _lowerCamelCase	: Optional[Any]       =       self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf    , (str, bytes, os.PathLike)					):
            with open(self.path_or_buf    , '''wb+'''					) as buffer:
                _lowerCamelCase	: int       =       self._write(file_obj=_lowercase    , batch_size=_lowercase    , **self.parquet_writer_kwargs					)
        else:
            _lowerCamelCase	: List[Any]       =       self._write(file_obj=self.path_or_buf    , batch_size=_lowercase    , **self.parquet_writer_kwargs					)
        return written







    def 				a__					(			self    , _lowercase    , _lowercase    , **_lowercase					)	->	int:
        _lowerCamelCase	: List[Any]       =       0
        _lowerCamelCase	: Union[str, Any]       =       parquet_writer_kwargs.pop('''path_or_buf'''    , _lowercase					)
        _lowerCamelCase	: List[str]       =       self.dataset.features.arrow_schema

        _lowerCamelCase	: int       =       pq.ParquetWriter(_lowercase    , schema=_lowercase    , **_lowercase					)

        for offset in logging.tqdm(
            range(0    , len(self.dataset					)    , _lowercase					)    , unit='''ba'''    , disable=not logging.is_progress_bar_enabled()    , desc='''Creating parquet from Arrow format'''    , ):
            _lowerCamelCase	: Tuple       =       query_table(
                table=self.dataset._data    , key=slice(_lowercase    , offset + batch_size					)    , indices=self.dataset._indices if self.dataset._indices is not None else None    , )
            writer.write_table(_lowercase					)
            written += batch.nbytes
        writer.close()
        return written










def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for data in source_data:
								for i, el in enumerate(lowerCamelCase_							):
												if len(lowerCamelCase_							) < i + 1:
																data_lists.append([]							)
												data_lists[i].append(float(lowerCamelCase_							)							)
				return data_lists






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for dlist, weight in zip(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     min(lowerCamelCase_							)
								lowercase__  					=     max(lowerCamelCase_							)

								lowercase__  					=     []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))							)
																except ZeroDivisionError:
																				score.append(1							)

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)							)
																except ZeroDivisionError:
																				score.append(0							)

        # weight not 0 or 1
								else:
												lowercase__  					=     F"""Invalid weight of {weight:f} provided"""
												raise ValueError(lowerCamelCase_							)

								score_lists.append(lowerCamelCase_							)

				return score_lists






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     [0 for i in range(len(score_lists[0]							)							)]

				for slist in score_lists:
								for j, ele in enumerate(lowerCamelCase_							):
												lowercase__  					=     final_scores[j] + ele

				return final_scores






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_data(lowerCamelCase_							)
				lowercase__  					=     calculate_each_score(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     generate_final_scores(lowerCamelCase_							)

				# append scores to source data
				for i, ele in enumerate(lowerCamelCase_							):
								source_data[i].append(lowerCamelCase_							)

				return source_data




