
'''simple docstring'''






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     Optional[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = tmp_path / "file.csv"
      _lowerCamelCase					: Optional[int]		    = textwrap.dedent(
          "\\n        header1,header2\n        1,2\n        10,20\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Any		    = tmp_path / "malformed_file.csv"
      _lowerCamelCase					: Any		    = textwrap.dedent(
          "\\n        header1,header2\n        1,2\n        10,20,\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: int		    = tmp_path / "csv_with_image.csv"
      _lowerCamelCase					: int		    = textwrap.dedent(
          F'\\n        image\n        {image_file}\n        '				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = tmp_path / "csv_with_label.csv"
      _lowerCamelCase					: int		    = textwrap.dedent(
          "\\n        label\n        good\n        bad\n        good\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







@pytest.fixture
def 		A_							(						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = tmp_path / "csv_with_int_list.csv"
      _lowerCamelCase					: Any		    = textwrap.dedent(
          "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "				)
      with open(_lowerCAmelCase					,						"w"				) as f:
            f.write(_lowerCAmelCase				)
      return str(_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = Csv()
      _lowerCamelCase					: Any		    = csv._generate_tables([[csv_file, malformed_csv_file]]				)
      with pytest.raises(_lowerCAmelCase					,						match="Error tokenizing data"				):
            for _ in generator:
                  pass
      assert any(
          record.levelname == "ERROR"
          and "Failed to read file" in record.message
          and os.path.basename(_lowerCAmelCase				) in record.message
          for record in caplog.records				)







@require_pil
def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      with open(_lowerCAmelCase					,						encoding="utf-8"				) as f:
            _lowerCamelCase					: Any		    = f.read().splitlines()[1]
      _lowerCamelCase					: Optional[Any]		    = Csv(encoding="utf-8"					,						features=Features({"image": Image()}				)				)
      _lowerCamelCase					: Union[str, Any]		    = csv._generate_tables([[csv_file_with_image]]				)
      _lowerCamelCase					: List[str]		    = pa.concat_tables([table for _, table in generator]				)
      assert pa_table.schema.field("image"				).type == Image()()
      _lowerCamelCase					: int		    = pa_table.to_pydict()["image"]
      assert generated_content == [{"path": image_file, "bytes": None}]







def 		A_							(						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




      with open(_lowerCAmelCase					,						encoding="utf-8"				) as f:
            _lowerCamelCase					: List[Any]		    = f.read().splitlines()[1:]
      _lowerCamelCase					: int		    = Csv(encoding="utf-8"					,						features=Features({"label": ClassLabel(names=["good", "bad"]				)}				)				)
      _lowerCamelCase					: Tuple		    = csv._generate_tables([[csv_file_with_label]]				)
      _lowerCamelCase					: int		    = pa.concat_tables([table for _, table in generator]				)
      assert pa_table.schema.field("label"				).type == ClassLabel(names=["good", "bad"]				)()
      _lowerCamelCase					: Union[str, Any]		    = pa_table.to_pydict()["label"]
      assert generated_content == [ClassLabel(names=["good", "bad"]				).straint(_lowerCAmelCase				) for label in labels]







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = Csv(encoding="utf-8"					,						sep=","					,						converters={"int_list": lambda _lowerCAmelCase				: [int(_lowerCAmelCase				) for i in x.split()]}				)
      _lowerCamelCase					: List[Any]		    = csv._generate_tables([[csv_file_with_int_list]]				)
      _lowerCamelCase					: Optional[int]		    = pa.concat_tables([table for _, table in generator]				)
      assert pa.types.is_list(pa_table.schema.field("int_list"				).type				)
      _lowerCamelCase					: Optional[Any]		    = pa_table.to_pydict()["int_list"]
      assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

"""simple docstring"""





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    10




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    [1, 2, 3, 4]
								A_								=    [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(_snake_case							, self.block_size							, 0						)							, _snake_case						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(_snake_case							, self.block_size							, 0						)							, _snake_case						)




			def   lowerCamelCase__  (					self    :       str						)   ->			Dict:







								"""simple docstring"""
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(_snake_case							, self.block_size							, 0						)							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Dict:







								"""simple docstring"""
								A_								=    "It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
								A_			,     A_								=    process_story(_snake_case						)
								self.assertEqual(_snake_case							, []						)




			def   lowerCamelCase__  (					self    :       str						)   ->			str:







								"""simple docstring"""
								A_								=    ""
								A_			,     A_								=    process_story(_snake_case						)
								self.assertEqual(_snake_case							, []						)
								self.assertEqual(_snake_case							, []						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    (
								    "It was the year of Our Lord one thousand seven hundred and "
								    "seventy-five\n\nSpiritual revelations were conceded to England "
								    "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
								)
								A_			,     A_								=    process_story(_snake_case						)

								A_								=    [
								    "It was the year of Our Lord one thousand seven hundred and seventy-five.",
								    "Spiritual revelations were conceded to England at that favoured period, as at this.",
								]
								self.assertEqual(_snake_case							, _snake_case						)

								A_								=    ["It was the best of times."]
								self.assertEqual(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    torch.tensor([1, 2, 3, 4]						)
								A_								=    torch.tensor([1, 1, 1, 1]						)
								np.testing.assert_array_equal(build_mask(_snake_case							, 0						).numpy()							, expected.numpy()						)




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    torch.tensor([1, 2, 3, 4, 23, 23, 23]						)
								A_								=    torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
								np.testing.assert_array_equal(build_mask(_snake_case							, 23						).numpy()							, expected.numpy()						)




			def   lowerCamelCase__  (					self    :       str						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    torch.tensor([8, 2, 3, 4, 1, 1, 1]						)
								A_								=    torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
								np.testing.assert_array_equal(build_mask(_snake_case							, 1						).numpy()							, expected.numpy()						)






			def   lowerCamelCase__  (					self    :       int						)   ->			List[str]:







								"""simple docstring"""
								A_								=    101
								A_								=    torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]						)
								A_								=    torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]						)

								A_								=    compute_token_type_ids(_snake_case							, _snake_case						)
								np.testing.assert_array_equal(_snake_case							, _snake_case						)




