





"""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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





@pytest.fixture
def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Dict										=  tmp_path / 'malformed_file.csv'
  A_  :       Union[str, Any]										=  textwrap.dedent(
      '\\n        header1,header2\n        1,2\n        10,20,\n        '					)
  with open(__UpperCamelCase ,					'w'					) as f:
    f.write(__UpperCamelCase					)
  return str(__UpperCamelCase					)





@pytest.fixture
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  tmp_path / 'csv_with_image.csv'
  A_  :       str										=  textwrap.dedent(
      f"""\
        image
        {image_file}
        """					)
  with open(__UpperCamelCase ,					'w'					) as f:
    f.write(__UpperCamelCase					)
  return str(__UpperCamelCase					)





@pytest.fixture
def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  tmp_path / 'csv_with_label.csv'
  A_  :       Optional[Any]										=  textwrap.dedent(
      '\\n        label\n        good\n        bad\n        good\n        '					)
  with open(__UpperCamelCase ,					'w'					) as f:
    f.write(__UpperCamelCase					)
  return str(__UpperCamelCase					)





@pytest.fixture
def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       str										=  tmp_path / 'csv_with_int_list.csv'
  A_  :       Optional[Any]										=  textwrap.dedent(
      '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '					)
  with open(__UpperCamelCase ,					'w'					) as f:
    f.write(__UpperCamelCase					)
  return str(__UpperCamelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  Csv()
  A_  :       Union[str, Any]										=  csv._generate_tables([[csv_file, malformed_csv_file]]					)
  with pytest.raises(__UpperCamelCase ,					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(__UpperCamelCase					) in record.message
      for record in caplog.records					)





@require_pil
def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  Csv(encoding='utf-8' ,					sep=',' ,					converters={'int_list': lambda _UpperCAmelCase					: [int(__UpperCamelCase					) for i in x.split()]}					)
  A_  :       Optional[int]										=  csv._generate_tables([[csv_file_with_int_list]]					)
  A_  :       Optional[Any]										=  pa.concat_tables([table for _, table in generator]					)
  assert pa.types.is_list(pa_table.schema.field('int_list'					).type					)
  A_  :       Optional[int]										=  pa_table.to_pydict()['int_list']
  assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
