






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											=      datasets.utils.logging.get_logger(__name__)




@dataclass
class 	lowerCamelCase__				(							datasets.BuilderConfig ):
      lowerCamelCase_							:   Optional[datasets.Features]											=	None
      lowerCamelCase_							:   str											=	"utf-8"
      lowerCamelCase_							:   Optional[str]											=	None
      lowerCamelCase_							:   int											=	10 << 20  # 10MB
      lowerCamelCase_							:   bool											=	False
      lowerCamelCase_							:   str											=	"line"




class 	lowerCamelCase__				(							datasets.ArrowBasedBuilder ):
      lowerCamelCase_							:   Dict											=	TextConfig






      def      UpperCAmelCase_     (self					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       return datasets.DatasetInfo(features=self.config.features		)






      def      UpperCAmelCase_     (self					:    Optional[Any]		,		_snake_case					:    Optional[Any]		)       ->   Any:


       """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}'		)
       lowerCamelCase_							:    Optional[int]							=     dl_manager.download_and_extract(self.config.data_files		)
       if isinstance(_snake_case		,		(str, list, tuple)		):
        lowerCamelCase_							:    Union[str, Any]							=     data_files
        if isinstance(_snake_case		,		_snake_case		):
         lowerCamelCase_							:    List[str]							=     [files]
        lowerCamelCase_							:    str							=     [dl_manager.iter_files(_snake_case		) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,		gen_kwargs={'files': files}		)]
       lowerCamelCase_							:    List[str]							=     []
       for split_name, files in data_files.items():
        if isinstance(_snake_case		,		_snake_case		):
         lowerCamelCase_							:    List[str]							=     [files]
        lowerCamelCase_							:    str							=     [dl_manager.iter_files(_snake_case		) for file in files]
        splits.append(datasets.SplitGenerator(name=_snake_case		,		gen_kwargs={'files': files}		)		)
       return splits






      def      UpperCAmelCase_     (self					:    Union[str, Any]		,		_snake_case					:    pa.Table		)       ->   pa.Table:


       """simple docstring"""






       if self.config.features is not None:
        lowerCamelCase_							:    Tuple							=     self.config.features.arrow_schema
        if all(not require_storage_cast(_snake_case		) for feature in self.config.features.values()		):
         # cheaper cast
         lowerCamelCase_							:    Union[str, Any]							=     pa_table.cast(_snake_case		)
        else:
         # more expensive cast; allows str <-> int/float or str to Audio for example
         lowerCamelCase_							:    List[str]							=     table_cast(_snake_case		,		_snake_case		)
        return pa_table
       else:
        return pa_table.cast(pa.schema({'text': pa.string()}		)		)






      def      UpperCAmelCase_     (self					:    Optional[int]		,		_snake_case					:    str		)       ->   str:


       """simple docstring"""






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








from dataclasses import dataclass

UpperCamelCase											=      '''0.1'''
UpperCamelCase											=      '''Lucia Harcekova'''




@dataclass
class 	lowerCamelCase__				:
      lowerCamelCase_							:   int
      lowerCamelCase_							:   int
      lowerCamelCase_							:   str






      def __repr__(self					:    Tuple		)       ->   str:


       """simple docstring"""






       return f'({self.offset}, {self.length}, {self.indicator})'




class 	lowerCamelCase__				:






      def __init__(self					:    Optional[int]		,		_snake_case					:    int = 13		,		_snake_case					:    int = 6		)       ->   None:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     window_size
       lowerCamelCase_							:    List[str]							=     lookahead_buffer_size
       lowerCamelCase_							:    Optional[int]							=     self.window_size - self.lookahead_buffer_size






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		)       ->   list[Token]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     []
       lowerCamelCase_							:    List[str]							=     ''

       # while there are still characters in text to compress
       while text:
        # find the next encoding phrase
        # - triplet with offset, length, indicator (the next encoding character)
        lowerCamelCase_							:    List[Any]							=     self._find_encoding_token(_snake_case		,		_snake_case		)

        # update the search buffer:
        # - add new characters from text into it
        # - check if size exceed the max search buffer size, if so, drop the
        #   oldest elements
        search_buffer += text[: token.length + 1]
        if len(_snake_case		) > self.search_buffer_size:
         lowerCamelCase_							:    int							=     search_buffer[-self.search_buffer_size :]

        # update the text
        lowerCamelCase_							:    str							=     text[token.length + 1 :]

        # append the token to output
        output.append(_snake_case		)

       return output






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    list[Token]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     ''

       for token in tokens:
        for _ in range(token.length		):
         output += output[-token.offset]
        output += token.indicator

       return output






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    str		,		_snake_case					:    str		)       ->   Token:


       """simple docstring"""






       if not text:
        raise ValueError('We need some text to work with.'		)

       # Initialise result parameters to default values
       lowerCamelCase_		,      lowerCamelCase_							:    List[str]							=     0, 0

       if not search_buffer:
        return Token(_snake_case		,		_snake_case		,		text[length]		)

       for i, character in enumerate(_snake_case		):
        lowerCamelCase_							:    Union[str, Any]							=     len(_snake_case		) - i
        if character == text[0]:
         lowerCamelCase_							:    str							=     self._match_length_from_index(_snake_case		,		_snake_case		,		0		,		_snake_case		)
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
          lowerCamelCase_		,      lowerCamelCase_							:    str							=     found_offset, found_length

       return Token(_snake_case		,		_snake_case		,		text[length]		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   int:


       """simple docstring"""






       if not text or text[text_index] != window[window_index]:
        return 0
       return 1 + self._match_length_from_index(
           _snake_case		,		window + text[text_index]		,		text_index + 1		,		window_index + 1		)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       UpperCamelCase											=      LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

       # Example
       UpperCamelCase											=      '''cabracadabrarrarrad'''
       UpperCamelCase											=      lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       UpperCamelCase											=      lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
