






"""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        lowerCAmelCase (      UpperCamelCase_:							Features					)       ->							Optional[int]:







  '''simple docstring'''







  _a							=	np.inf

  def set_batch_size(UpperCamelCase_:							FeatureType					) -> None:
    nonlocal batch_size
    if isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
    elif isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
    elif isinstance(UpperCamelCase_     , UpperCamelCase_					) and feature.dtype == "binary":
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

  _visit(UpperCamelCase_     , UpperCamelCase_					)

  return None if batch_size is np.inf else batch_size




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     a_     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = False     ,     a_ = False     ,     a_ = None     ,     **a_     ,     )	->Optional[int]:







        '''simple docstring'''


        super().__init__(
            a_     ,     split=a_     ,     features=a_     ,     cache_dir=a_     ,     keep_in_memory=a_     ,     streaming=a_     ,     num_proc=a_     ,     **a_     ,     )
        _a							=	path_or_paths if isinstance(a_     ,     a_	) else {self.split: path_or_paths}
        _a							=	_PACKAGED_DATASETS_MODULES["parquet"][1]
        _a							=	Parquet(
            cache_dir=a_     ,     data_files=a_     ,     features=a_     ,     hash=a_     ,     **a_     ,     )






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        if self.streaming:
          _a							=	self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
          _a							=	None
          _a							=	None
          _a							=	None
          _a							=	None

          self.builder.download_and_prepare(
              download_config=a_     ,     download_mode=a_     ,     verification_mode=a_     ,     base_path=a_     ,     num_proc=self.num_proc     ,     )
          _a							=	self.builder.as_dataset(
              split=self.split     ,     verification_mode=a_     ,     in_memory=self.keep_in_memory	)
        return dataset







class    lowercase_    :






      def __init__(    self     ,     a_     ,     a_     ,     a_ = None     ,     **a_     ,     )	->int:







        '''simple docstring'''


        _a							=	dataset
        _a							=	path_or_buf
        _a							=	batch_size or get_writer_batch_size(dataset.features	)
        _a							=	parquet_writer_kwargs






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	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:
            _a							=	self._write(file_obj=a_     ,     batch_size=a_     ,     **self.parquet_writer_kwargs	)
        else:
          _a							=	self._write(file_obj=self.path_or_buf     ,     batch_size=a_     ,     **self.parquet_writer_kwargs	)
        return written






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     **a_	)	->int:







        '''simple docstring'''


        _a							=	0
        _a							=	parquet_writer_kwargs.pop("path_or_buf"     ,     a_	)
        _a							=	self.dataset.features.arrow_schema

        _a							=	pq.ParquetWriter(a_     ,     schema=a_     ,     **a_	)

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










"""simple docstring"""




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def        lowerCAmelCase (      UpperCamelCase_:							ndarray					)       ->							float:







  '''simple docstring'''







  return np.dot(UpperCamelCase_     , UpperCamelCase_					)




class    lowercase_    :






      def __init__(    self     ,     *,
          a_ = np.inf     ,     a_ = "linear"     ,     a_ = 0.0     ,     )	->None:







        '''simple docstring'''


        _a							=	regularization
        _a							=	gamma
        if kernel == "linear":
          _a							=	self.__linear
        elif kernel == "rbf":
          if self.gamma == 0:
            raise ValueError("rbf kernel requires gamma"	)
          if not isinstance(self.gamma     ,     (float, int)	):
            raise ValueError("gamma must be float or int"	)
          if not self.gamma > 0:
            raise ValueError("gamma must be > 0"	)
          _a							=	self.__rbf
          # in the future, there could be a default value like in sklearn
          # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
          # previously it was 1/(n_features)
        else:
          _a							=	f'''Unknown kernel: {kernel}'''
          raise ValueError(a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->float:







        '''simple docstring'''


        return np.dot(a_     ,     a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->float:







        '''simple docstring'''


        return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->None:







        '''simple docstring'''


        _a							=	observations
        _a							=	classes

        # using Wolfe's Dual to calculate w.
        # Primal problem: minimize 1/2*norm_squared(w)
        #   constraint: yn(w . xn + b) >= 1
        #
        # With l a vector
        # Dual problem: maximize sum_n(ln) -
        #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
        #   constraint: self.C >= ln >= 0
        #           and sum_n(ln*yn) = 0
        # Then we get w using w = sum_n(ln*yn*xn)
        # At the end we can get b ~= mean(yn - w . xn)
        #
        # Since we use kernels, we only need l_star to calculate b
        # and to classify observations

        ((_a)			,	)							=	np.shape(a_	)

        def to_minimize(a_	) -> float:
          _a							=	0
          ((_a)			,	)							=	np.shape(a_	)
          for i in range(a_	):
            for j in range(a_	):
              s += (
                  candidate[i]
                  * candidate[j]
                  * classes[i]
                  * classes[j]
                  * self.kernel(observations[i]     ,     observations[j]	)
              )
          return 1 / 2 * s - sum(a_	)

        _a							=	LinearConstraint(a_     ,     0     ,     0	)
        _a							=	Bounds(0     ,     self.regularization	)

        _a							=	minimize(
            a_     ,     np.ones(a_	)     ,     bounds=a_     ,     constraints=[ly_contraint]	).x
        _a							=	l_star

        # calculating mean offset of separation plane to points
        _a							=	0
        for i in range(a_	):
          for j in range(a_	):
            s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                observations[i]     ,     observations[j]	)
        _a							=	s / n






      def 			lowerCamelCase__    (    self     ,     a_	)	->int:







        '''simple docstring'''


        _a							=	sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]     ,     a_	)
            for n in range(len(self.classes	)	)	)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()


