





"""simple docstring"""




from .number_theory.prime_numbers import next_prime

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Tuple									=    size_table
        A_    :			List[str]									=    [None] * self.size_table
        A_    :			Union[str, Any]									=    0.75 if lim_charge is None else lim_charge
        A_    :			Dict									=    1 if charge_factor is None else charge_factor
        A_    :			list									=    []
        A_    :			dict									=    {}

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self._keys

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return sum(1 for slot in self.values if slot is not None    ) / (
            self.size_table * self.charge_factor
        )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return key % self.size_table

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        print(F"""step {step_ord}"""    )
        print(list(range(len(self.values    )    )    )    )
        print(self.values    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    1
        A_    :			Dict									=    values
        for value in values:
          self.insert_data(snake_case_    )
          self._step_by_step(snake_case_    )
          i += 1

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			int									=    data
        A_    :			int									=    data

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    self.hash_function(key + 1    )

        while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(snake_case_    ) > 0:
            A_    :			Dict									=    self.hash_function(new_key + 1    )
          else:
            A_    :			List[str]									=    None
            break

        return new_key

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    [value for value in self.values if value is not None]
        A_    :			Dict									=    next_prime(self.size_table   , factor=2    )
        self._keys.clear()
        A_    :			List[str]									=    [None] * self.size_table  # hell's pointers D: don't DRY ;/
        for value in survivor_values:
          self.insert_data(snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    self.hash_function(snake_case_    )

        if self.values[key] is None:
          self._set_value(snake_case_   , snake_case_    )

        elif self.values[key] == data:
          pass

        else:
          A_    :			Dict									=    self._collision_resolution(snake_case_   , snake_case_    )
          if collision_resolution is not None:
            self._set_value(snake_case_   , snake_case_    )
          else:
            self.rehashing()
            self.insert_data(snake_case_    )






"""simple docstring"""




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = False   , snake_case_ = False   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(
            snake_case_   , split=snake_case_   , features=snake_case_   , cache_dir=snake_case_   , keep_in_memory=snake_case_   , streaming=snake_case_   , num_proc=snake_case_   , **snake_case_   , )
        A_    :			int									=    path_or_paths if isinstance(snake_case_   , snake_case_    ) else {self.split: path_or_paths}
        A_    :			List[str]									=    Csv(
            cache_dir=snake_case_   , data_files=snake_case_   , features=snake_case_   , **snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.streaming:
          A_    :			List[str]									=    self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
          A_    :			List[Any]									=    None
          A_    :			Optional[int]									=    None
          A_    :			Optional[int]									=    None
          A_    :			List[str]									=    None

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


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        if num_proc is not None and num_proc <= 0:
          raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""    )

        A_    :			Optional[int]									=    dataset
        A_    :			List[str]									=    path_or_buf
        A_    :			Optional[int]									=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A_    :			Optional[int]									=    num_proc
        A_    :			Optional[int]									=    'utf-8'
        A_    :			int									=    to_csv_kwargs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.to_csv_kwargs.pop('path_or_buf'   , snake_case_    )
        A_    :			str									=    self.to_csv_kwargs.pop('header'   , snake_case_    )
        A_    :			Optional[Any]									=    self.to_csv_kwargs.pop('index'   , snake_case_    )

        if isinstance(self.path_or_buf   , (str, bytes, os.PathLike)    ):
          with open(self.path_or_buf   , 'wb+'    ) as buffer:
            A_    :			Optional[Any]									=    self._write(file_obj=snake_case_   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        else:
          A_    :			int									=    self._write(file_obj=self.path_or_buf   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        return written

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Union[str, Any]									=    args

        A_    :			Dict									=    query_table(
            table=self.dataset.data   , key=slice(snake_case_   , offset + self.batch_size    )   , indices=self.dataset._indices   , )
        A_    :			List[str]									=    batch.to_pandas().to_csv(
            path_or_buf=snake_case_   , header=header if (offset == 0) else False   , index=snake_case_   , **snake_case_    )
        return csv_str.encode(self.encoding    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    0

        if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0   , len(self.dataset    )   , self.batch_size    )   , unit='ba'   , disable=not logging.is_progress_bar_enabled()   , desc='Creating CSV from Arrow format'   , ):
            A_    :			Optional[int]									=    self._batch_csv((offset, header, index, to_csv_kwargs)    )
            written += file_obj.write(snake_case_    )

        else:
          A_		,			A_    :			Optional[int]									=    len(self.dataset    ), self.batch_size
          with multiprocessing.Pool(self.num_proc    ) as pool:
            for csv_str in logging.tqdm(
                pool.imap(
                    self._batch_csv   , [(offset, header, index, to_csv_kwargs) for offset in range(0   , snake_case_   , snake_case_    )]   , )   , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   , unit='ba'   , disable=not logging.is_progress_bar_enabled()   , desc='Creating CSV from Arrow format'   , ):
              written += file_obj.write(snake_case_    )

        return written