

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			Optional[Any]							):





  '''simple docstring'''







  assert isinstance(_A     ,				_A							)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('''keep_in_memory'''     ,				[False, True]							)
def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			str     ,				_A				:			Any							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a__  =CsvDatasetReader(_A     ,				cache_dir=_A     ,				keep_in_memory=_A							).read()
  _check_csv_dataset(_A     ,				_A							)




@pytest.mark.parametrize(
    '''features'''     ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]     ,				)
def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Tuple     ,				_A				:			str							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  a__  =features.copy() if features else default_expected_features
  a__  =(
      Features({feature: Value(_A							) for feature, dtype in features.items()}							) if features is not None else None
  )
  a__  =CsvDatasetReader(_A     ,				features=_A     ,				cache_dir=_A							).read()
  _check_csv_dataset(_A     ,				_A							)




@pytest.mark.parametrize('''split'''     ,				[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			int     ,				_A				:			List[Any]							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  a__  =CsvDatasetReader(_A     ,				cache_dir=_A     ,				split=_A							).read()
  _check_csv_dataset(_A     ,				_A							)
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''     ,				[str, list]							)
def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			Union[str, Any]     ,				_A				:			str							):





  '''simple docstring'''







  if issubclass(_A     ,				_A							):
    a__  =csv_path
  elif issubclass(_A     ,				_A							):
    a__  =[csv_path]
  a__  =tmp_path / '''cache'''
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  a__  =CsvDatasetReader(_A     ,				cache_dir=_A							).read()
  _check_csv_dataset(_A     ,				_A							)




def       UpperCAmelCase__    (      _A				:			List[str]     ,				_A				:			int     ,				_A				:			Union[str, Any]=("train",)							):





  '''simple docstring'''







  assert isinstance(_A     ,				_A							)
  for split in splits:
    a__  =dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('''keep_in_memory'''     ,				[False, True]							)
def       UpperCAmelCase__    (      _A				:			Any     ,				_A				:			Optional[Any]     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a__  =CsvDatasetReader({'''train''': csv_path}     ,				cache_dir=_A     ,				keep_in_memory=_A							).read()
  _check_csv_datasetdict(_A     ,				_A							)




@pytest.mark.parametrize(
    '''features'''     ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]     ,				)
def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Optional[int]     ,				_A				:			Optional[Any]							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  a__  =features.copy() if features else default_expected_features
  a__  =(
      Features({feature: Value(_A							) for feature, dtype in features.items()}							) if features is not None else None
  )
  a__  =CsvDatasetReader({'''train''': csv_path}     ,				features=_A     ,				cache_dir=_A							).read()
  _check_csv_datasetdict(_A     ,				_A							)




@pytest.mark.parametrize('''split'''     ,				[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			List[Any]     ,				_A				:			Optional[Any]							):





  '''simple docstring'''







  if split:
    a__  ={split: csv_path}
  else:
    a__  ={'''train''': csv_path, '''test''': csv_path}
  a__  =tmp_path / '''cache'''
  a__  ={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  a__  =CsvDatasetReader(_A     ,				cache_dir=_A							).read()
  _check_csv_datasetdict(_A     ,				_A     ,				splits=list(path.keys()							)							)
  assert all(dataset[split].split == split for split in path.keys()							)




def       UpperCAmelCase__    (      _A				:			List[Any]							):





  '''simple docstring'''







  with open(_A     ,				encoding='''utf-8'''							) as csvfile:
    yield from csv.reader(_A							)




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Any							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  a__  =os.path.join(_A     ,				'''tmp.csv'''							)
  a__  =CsvDatasetReader({'''train''': csv_path}     ,				cache_dir=_A							).read()
  CsvDatasetWriter(dataset['''train''']     ,				_A     ,				num_proc=1							).write()

  a__  =iter_csv_file(_A							)
  a__  =iter_csv_file(_A							)

  for rowa, rowa in zip(_A     ,				_A							):
    assert rowa == rowa




def       UpperCAmelCase__    (      _A				:			Any     ,				_A				:			List[Any]							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  a__  =os.path.join(_A     ,				'''tmp.csv'''							)
  a__  =CsvDatasetReader({'''train''': csv_path}     ,				cache_dir=_A							).read()
  CsvDatasetWriter(dataset['''train''']     ,				_A     ,				num_proc=2							).write()

  a__  =iter_csv_file(_A							)
  a__  =iter_csv_file(_A							)

  for rowa, rowa in zip(_A     ,				_A							):
    assert rowa == rowa




def       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			str							):





  '''simple docstring'''







  a__  =tmp_path / '''cache'''
  a__  =os.path.join(_A     ,				'''tmp.csv'''							)
  a__  =CsvDatasetReader({'''train''': csv_path}     ,				cache_dir=_A							).read()
  with pytest.raises(_A							):
    CsvDatasetWriter(dataset['''train''']     ,				_A     ,				num_proc=0							)



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."
