
"""simple docstring"""
import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							List[Any]				)  ->    Optional[int]:
  assert isinstance(lowerCamelCase__,						lowerCamelCase__				)
  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





@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory",						[False, True]				)
def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							str,						lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tmp_path / "cache"
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _SCREAMING_SNAKE_CASE   :				str							    = SqlDatasetReader(
        "dataset",						"sqlite:///" + sqlite_path,						cache_dir=lowerCamelCase__,						keep_in_memory=lowerCamelCase__				).read()
  _check_sql_dataset(lowerCamelCase__,						lowerCamelCase__				)





@require_sqlalchemy
@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    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = tmp_path / "cache"
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = features.copy() if features else default_expected_features
  _SCREAMING_SNAKE_CASE   :				str							    = (
      Features({feature: Value(lowerCamelCase__				) for feature, dtype in features.items()}				) if features is not None else None
  )
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = SqlDatasetReader("dataset",						"sqlite:///" + sqlite_path,						features=lowerCamelCase__,						cache_dir=lowerCamelCase__				).read()
  _check_sql_dataset(lowerCamelCase__,						lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple				)  ->    List[Any]:
  with contextlib.closing(sqlitea.connect(lowerCamelCase__				)				) as con:
    _SCREAMING_SNAKE_CASE   :				int							    = con.cursor()
    cur.execute("SELECT * FROM dataset"				)
    for row in cur:
      yield row





@require_sqlalchemy
def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							str				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tmp_path / "cache"
  _SCREAMING_SNAKE_CASE   :				Any							    = os.path.join(lowerCamelCase__,						"tmp.sql"				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = SqlDatasetReader("dataset",						"sqlite:///" + sqlite_path,						cache_dir=lowerCamelCase__				).read()
  SqlDatasetWriter(lowerCamelCase__,						"dataset",						"sqlite:///" + output_sqlite_path,						num_proc=1				).write()

  _SCREAMING_SNAKE_CASE   :				str							    = iter_sql_file(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = iter_sql_file(lowerCamelCase__				)

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





@require_sqlalchemy
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							List[Any]				)  ->    Optional[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = tmp_path / "cache"
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = os.path.join(lowerCamelCase__,						"tmp.sql"				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = SqlDatasetReader("dataset",						"sqlite:///" + sqlite_path,						cache_dir=lowerCamelCase__				).read()
  SqlDatasetWriter(lowerCamelCase__,						"dataset",						"sqlite:///" + output_sqlite_path,						num_proc=2				).write()

  _SCREAMING_SNAKE_CASE   :				int							    = iter_sql_file(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = iter_sql_file(lowerCamelCase__				)

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





@require_sqlalchemy
def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							int				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = tmp_path / "cache"
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = os.path.join(lowerCamelCase__,						"tmp.sql"				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = SqlDatasetReader("dataset",						"sqlite:///" + sqlite_path,						cache_dir=lowerCamelCase__				).read()
  with pytest.raises(lowerCamelCase__				):
    SqlDatasetWriter(lowerCamelCase__,						"dataset",						"sqlite:///" + output_sqlite_path,						num_proc=0				).write()





"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

  def is_too_big(lowerCamelCase__							:							List[Any]				):
    return tok(lowerCamelCase__,						return_tensors="pt"				).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]				):
    _SCREAMING_SNAKE_CASE   :				int							    = new_src + " " + src
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_tgt + " " + tgt
    if is_too_big(lowerCamelCase__				) or is_too_big(lowerCamelCase__				):  # cant fit, finalize example
      finished_src.append(lowerCamelCase__				)
      finished_tgt.append(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = src, tgt
    else:  # can fit, keep adding
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = cand_src, cand_tgt

    # cleanup
  if new_src:
    assert new_tgt
    finished_src.append(lowerCamelCase__				)
    finished_tgt.append(lowerCamelCase__				)
  return finished_src, finished_tgt





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Path,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[str]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = Path(lowerCamelCase__				)
  save_path.mkdir(exist_ok=lowerCamelCase__				)
  for split in ["train"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    _SCREAMING_SNAKE_CASE   :				Any							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE   :				int							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = pack_examples(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
    print(f'''packed {split} split from {len(lowerCamelCase__				)} examples -> {len(lowerCamelCase__				)}.'''				)
    Path(save_path / f'''{split}.source'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
    Path(save_path / f'''{split}.target'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
  for split in ["val", "test"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.source'''				)
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.target'''				)





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



