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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]     )							-> Union[str, Any]:







    """simple docstring"""





    SCREAMING_SNAKE_CASE__      							=				tmp_path / 'file.csv'
    SCREAMING_SNAKE_CASE__      							=				textwrap.dedent(
        """\\n        header1,header2\n        1,2\n        10,20\n        """     )
    with open(snake_case__			,					"""w"""     ) as f:
        f.write(snake_case__     )
    return str(snake_case__     )



@pytest.fixture
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple     )							-> int:







    """simple docstring"""





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



@pytest.fixture
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Optional[int]     )							-> Union[str, Any]:







    """simple docstring"""





    SCREAMING_SNAKE_CASE__      							=				tmp_path / 'csv_with_image.csv'
    SCREAMING_SNAKE_CASE__      							=				textwrap.dedent(
        f"""\
        image
        {image_file}
        """     )
    with open(snake_case__			,					"""w"""     ) as f:
        f.write(snake_case__     )
    return str(snake_case__     )



@pytest.fixture
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]     )							-> List[Any]:







    """simple docstring"""





    SCREAMING_SNAKE_CASE__      							=				tmp_path / 'csv_with_label.csv'
    SCREAMING_SNAKE_CASE__      							=				textwrap.dedent(
        """\\n        label\n        good\n        bad\n        good\n        """     )
    with open(snake_case__			,					"""w"""     ) as f:
        f.write(snake_case__     )
    return str(snake_case__     )



@pytest.fixture
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> int:







    """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Any     )							-> str:







    """simple docstring"""





    SCREAMING_SNAKE_CASE__      							=				Csv()
    SCREAMING_SNAKE_CASE__      							=				csv._generate_tables([[csv_file, malformed_csv_file]]     )
    with pytest.raises(snake_case__			,					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(snake_case__     ) in record.message
        for record in caplog.records     )



@require_pil
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple     )							-> Optional[int]:







    """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     )							-> Optional[Any]:







    """simple docstring"""





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






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict     )							-> Optional[Any]:







    """simple docstring"""





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

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


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

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
