

"""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  (  UpperCamelCase_					:	Tuple)     ->						Any:
   '''simple docstring'''


   __lowercase								=						tmp_path / """file.csv"""
   __lowercase								=						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  (  UpperCamelCase_					:	str)     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						tmp_path / """malformed_file.csv"""
   __lowercase								=						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  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Optional[int])     ->						str:
   '''simple docstring'''


   __lowercase								=						tmp_path / """csv_with_image.csv"""
   __lowercase								=						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  (  UpperCamelCase_					:	Optional[Any])     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						tmp_path / """csv_with_label.csv"""
   __lowercase								=						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  (  UpperCamelCase_					:	Union[str, Any])     ->						List[Any]:
   '''simple docstring'''


   __lowercase								=						tmp_path / """csv_with_int_list.csv"""
   __lowercase								=						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  (  UpperCamelCase_					:	List[str],      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Tuple)     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						Csv()
   __lowercase								=						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  (  UpperCamelCase_					:	Tuple)     ->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Dict)     ->						str:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	str)     ->						Optional[Any]:
   '''simple docstring'''


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







"""simple docstring"""





import re

from filelock import FileLock


try:
   import nltk

   _a							=							True
except (ImportError, ModuleNotFoundError):
   _a							=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   re.sub("<n>",      "",      UpperCamelCase_)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(UpperCamelCase_))




