




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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       float:



       """simple docstring"""

       return np.dot(lowercase_ ,				lowercase_						)


class 				UpperCamelCase_    :

    '''simple docstring'''






    def __init__(			self      :	Union[str, Any]					, *,
        UpperCAmelCase__      :	float = np.inf					, UpperCAmelCase__      :	str = "linear"					, UpperCAmelCase__      :	float = 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(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	ndarray					, UpperCAmelCase__      :	ndarray)						->float:





           '''simple docstring'''

           return np.dot(UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	ndarray					, UpperCAmelCase__      :	ndarray)						->float:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	list[ndarray]					, UpperCAmelCase__      :	ndarray)						->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(UpperCAmelCase__)

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

           A__       =    LinearConstraint(UpperCAmelCase__					, 0					, 0)
           A__       =    Bounds(0					, self.regularization)

           A__       =    minimize(
               UpperCAmelCase__					, np.ones(UpperCAmelCase__)					, bounds=UpperCAmelCase__					, constraints=[ly_contraint]).x
           A__       =    l_star

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





    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	ndarray)						->int:





           '''simple docstring'''

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()








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


@dataclass
class 				UpperCamelCase_    ( datasets.BuilderConfig     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      "utf-8"
    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      True  # deprecated
    UpperCAmelCase__							  =      None  # deprecated
    UpperCAmelCase__							  =      10 << 20  # 10MB
    UpperCAmelCase__							  =      None


class 				UpperCamelCase_    ( datasets.ArrowBasedBuilder     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      JsonConfig


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->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''')
                  A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[Any])						->Dict:





           '''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__       =    dl_manager.download_and_extract(self.config.data_files)
           if isinstance(UpperCAmelCase__					, (str, list, tuple)):
                  A__       =    data_files
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={'''files''': files})]
           A__       =    []
           for split_name, files in data_files.items():
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCAmelCase__					, gen_kwargs={'''files''': files}))
           return splits


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





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Tuple)						->str:





           '''simple docstring'''

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

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

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

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


