

import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowerCamelCase									= datasets.utils.logging.get_logger(__name__)







@dataclass
class 						__magic_name__      (							datasets.BuilderConfig							):

   '''simple docstring'''




   lowerCamelCase__			:     Optional[datasets.Features]    =       None







class 						__magic_name__      (							datasets.ArrowBasedBuilder							):

   '''simple docstring'''




   lowerCamelCase__			:     Tuple    =       ArrowConfig



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """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(lowercase_,   (str, list, tuple)	):
       a__  =data_files
       if isinstance(lowercase_,   lowercase_	):
         a__  =[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       a__  =[dl_manager.iter_files(lowercase_	) 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(lowercase_,   lowercase_	):
         a__  =[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       a__  =[dl_manager.iter_files(lowercase_	) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
         for file in itertools.chain.from_iterable(lowercase_	):
           with open(lowercase_,   '''rb'''	) as f:
             a__  =datasets.Features.from_arrow_schema(pa.ipc.open_stream(lowercase_	).schema	)
           break
       splits.append(datasets.SplitGenerator(name=lowercase_,   gen_kwargs={'''files''': files}	)	)
     return splits



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   pa.Table:






     """simple docstring"""






     if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       a__  =table_cast(lowercase_,   self.info.features.arrow_schema	)
     return pa_table



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_	)	):
       with open(lowercase_,   '''rb'''	) as f:
         try:
           for batch_idx, record_batch in enumerate(pa.ipc.open_stream(lowercase_	)	):
             a__  =pa.Table.from_batches([record_batch]	)
             # 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 F"""{file_idx}_{batch_idx}""", self._cast_table(lowercase_	)
         except ValueError as e:
           logger.error(F"""Failed to read file '{file}' with error {type(lowercase_	)}: {e}"""	)
           raise



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






     return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_ = 13,   lowercase_ = 6	)							->   None:






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # 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)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # 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(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

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

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






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

     # Initialise result parameters to default values
     a__,				a__  =0, 0

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

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # 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:
           a__,				a__  =found_offset, found_length

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






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


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

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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