"""simple docstring"""


import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


__UpperCamelCase       :    Optional[Any]         =		datasets.utils.logging.get_logger(__name__)
@dataclass
class   UpperCAmelCase_	(				datasets.BuilderConfig   ):
    snake_case_											=      None
    snake_case_											=      "utf-8"
    snake_case_											=      None
    snake_case_											=      10 << 20  # 10MB
    snake_case_											=      False
    snake_case_											=      "line"
class   UpperCAmelCase_	(				datasets.ArrowBasedBuilder   ):
    snake_case_											=      TextConfig
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    int:
       return datasets.DatasetInfo(features=self.config.features		)
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       str		)						->    int:
       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}"""		)
       _lowercase							     =       dl_manager.download_and_extract(self.config.data_files		)
       if isinstance(_lowercase   ,						(str, list, tuple)		):
          _lowercase							     =       data_files
          if isinstance(_lowercase   ,						_lowercase		):
             _lowercase							     =       [files]
          _lowercase							     =       [dl_manager.iter_files(_lowercase		) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs={"files": files}		)]
       _lowercase							     =       []
       for split_name, files in data_files.items():
          if isinstance(_lowercase   ,						_lowercase		):
             _lowercase							     =       [files]
          _lowercase							     =       [dl_manager.iter_files(_lowercase		) for file in files]
          splits.append(datasets.SplitGenerator(name=_lowercase   ,						gen_kwargs={"files": files}		)		)
       return splits
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       pa.Table		)						->    pa.Table:
       if self.config.features is not None:
          _lowercase							     =       self.config.features.arrow_schema
          if all(not require_storage_cast(_lowercase		) for feature in self.config.features.values()		):
             # cheaper cast
             _lowercase							     =       pa_table.cast(_lowercase		)
          else:
             # more expensive cast; allows str <-> int/float or str to Audio for example
             _lowercase							     =       table_cast(_lowercase   ,						_lowercase		)
          return pa_table
       else:
          return pa_table.cast(pa.schema({"text": pa.string()}		)		)




    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Optional[Any]		)						->    Dict:
       _lowercase							     =       list(self.config.features		) if self.config.features is not None else ["text"]
       for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase		)		):
          # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
          with open(_lowercase   ,						encoding=self.config.encoding   ,						errors=self.config.errors		) as f:
             if self.config.sample_by == "line":
                _lowercase							     =       0
                while True:
                   _lowercase							     =       f.read(self.config.chunksize		)
                   if not batch:
                      break
                   batch += f.readline()  # finish current line
                   # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                   _lowercase							     =       StringIO(_lowercase		).readlines()
                   if not self.config.keep_linebreaks:
                      _lowercase							     =       [line.rstrip("\n"		) for line in batch]
                   _lowercase							     =       pa.Table.from_arrays([pa.array(_lowercase		)]   ,						names=_lowercase		)
                   # 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(_lowercase		)
                   batch_idx += 1
             elif self.config.sample_by == "paragraph":
                _lowercase							     =       0
                _lowercase							     =       ""
                while True:
                   _lowercase							     =       f.read(self.config.chunksize		)
                   if not new_batch:
                      break
                   batch += new_batch
                   batch += f.readline()  # finish current line
                   _lowercase							     =       batch.split("\n\n"		)
                   _lowercase							     =       pa.Table.from_arrays(
                       [pa.array([example for example in batch[:-1] if example]		)]   ,						names=_lowercase		)
                   # 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(_lowercase		)
                   batch_idx += 1
                   _lowercase							     =       batch[-1]
                if batch:
                   _lowercase							     =       pa.Table.from_arrays([pa.array([batch]		)]   ,						names=_lowercase		)
                   yield (file_idx, batch_idx), self._cast_table(_lowercase		)
             elif self.config.sample_by == "document":
                _lowercase							     =       f.read()
                _lowercase							     =       pa.Table.from_arrays([pa.array([text]		)]   ,						names=_lowercase		)
                yield file_idx, self._cast_table(_lowercase		)
"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__UpperCamelCase       :    str         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[Any]   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       str		)						->    None:
       warnings.warn(
           "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use ConvNextImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)