





"""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    lowercase_ (   __lowerCAmelCase							):

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






        '''simple docstring'''

        super().__init__(
            __SCREAMING_SNAKE_CASE      ,    split=__SCREAMING_SNAKE_CASE      ,    features=__SCREAMING_SNAKE_CASE      ,    cache_dir=__SCREAMING_SNAKE_CASE      ,    keep_in_memory=__SCREAMING_SNAKE_CASE      ,    streaming=__SCREAMING_SNAKE_CASE      ,    num_proc=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE      ,    )
        snake_case__										= path_or_paths if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
        snake_case__										= Csv(
            cache_dir=__SCREAMING_SNAKE_CASE      ,    data_files=__SCREAMING_SNAKE_CASE      ,    features=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE      ,    )

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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



class    lowercase_ :

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    **__SCREAMING_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."   )

        snake_case__										= dataset
        snake_case__										= path_or_buf
        snake_case__										= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        snake_case__										= num_proc
        snake_case__										= "utf-8"
        snake_case__										= to_csv_kwargs

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.to_csv_kwargs.pop("path_or_buf"      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= self.to_csv_kwargs.pop("header"      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= self.to_csv_kwargs.pop("index"      ,    __SCREAMING_SNAKE_CASE   )

        if isinstance(self.path_or_buf      ,    (str, bytes, os.PathLike)   ):
         with open(self.path_or_buf      ,    "wb+"   ) as buffer:
          snake_case__										= self._write(file_obj=__SCREAMING_SNAKE_CASE      ,    header=__SCREAMING_SNAKE_CASE      ,    index=__SCREAMING_SNAKE_CASE      ,    **self.to_csv_kwargs   )
        else:
         snake_case__										= self._write(file_obj=self.path_or_buf      ,    header=__SCREAMING_SNAKE_CASE      ,    index=__SCREAMING_SNAKE_CASE      ,    **self.to_csv_kwargs   )
        return written

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__							,      snake_case__							,      snake_case__							,      snake_case__										= args

        snake_case__										= query_table(
            table=self.dataset.data      ,    key=slice(__SCREAMING_SNAKE_CASE      ,    offset + self.batch_size   )      ,    indices=self.dataset._indices      ,    )
        snake_case__										= batch.to_pandas().to_csv(
            path_or_buf=__SCREAMING_SNAKE_CASE      ,    header=header if (offset == 0) else False      ,    index=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )
        return csv_str.encode(self.encoding   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= 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"      ,    ):
          snake_case__										= self._batch_csv((offset, header, index, to_csv_kwargs)   )
          written += file_obj.write(__SCREAMING_SNAKE_CASE   )

        else:
         snake_case__							,      snake_case__										= 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      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_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(__SCREAMING_SNAKE_CASE   )

        return written








"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


a__    :   str    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    *__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        warnings.warn(
            "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use ViltImageProcessor instead."      ,    __SCREAMING_SNAKE_CASE      ,    )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

