




import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]  ):  # picklable for multiprocessing


      '''simple docstring'''



      return x.sum()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):  # picklable for multiprocessing


      '''simple docstring'''



      return i + 1







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		42







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					{}
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					[1, 2]
       lowerCamelCase_	  =					{'a': 1, 'b': 2}
       lowerCamelCase_	  =					{'a': [1, 2], 'b': [3, 4]}
       lowerCamelCase_	  =					{'a': {'1': 1}, 'b': 2}
       lowerCamelCase_	  =					{'a': 1, 'b': 2, 'c': 3, 'd': 4}
       lowerCamelCase_	  =					{}
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					2
       lowerCamelCase_	  =					[2, 3]
       lowerCamelCase_	  =					{'a': 2, 'b': 3}
       lowerCamelCase_	  =					{'a': [2, 3], 'b': [4, 5]}
       lowerCamelCase_	  =					{'a': {'1': 2}, 'b': 3}
       lowerCamelCase_	  =					{'a': 2, 'b': 3, 'c': 4, 'd': 5}
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase						)		,							_lowerCAmelCase						)

       lowerCamelCase_	  =					2
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)

       lowerCamelCase_	  =					{'a': np.eye(2						), 'b': np.zeros(3						), 'c': np.ones(2						)}
       lowerCamelCase_	  =					{'a': 2, 'b': 0, 'c': 2}
       lowerCamelCase_	  =					{
           'a': np.eye(2						).astype(_lowerCAmelCase						),
           'b': np.zeros(3						).astype(_lowerCAmelCase						),
           'c': np.ones(2						).astype(_lowerCAmelCase						),
       }
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							map_numpy=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							map_numpy=_lowerCAmelCase						).items()}		,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,							)
       self.assertEqual(map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							map_numpy=_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)		,							_lowerCAmelCase						)
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(_lowerCAmelCase		,							_lowerCAmelCase		,							map_numpy=_lowerCAmelCase		,							num_proc=_lowerCAmelCase						).items()}		,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,							)
       with self.assertRaises(_lowerCAmelCase						):  # can't pickle a local lambda
             map_nested(lambda A_						: x + 1		,							_lowerCAmelCase		,							num_proc=_lowerCAmelCase						)

 def        a__				(       self      :						int						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					{'a': 1, 'b': 2}
       lowerCamelCase_	  =					{'a': 3, 'b': 4}
       lowerCamelCase_	  =					{'a': 5, 'b': 6}
       lowerCamelCase_	  =					sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]						)
       self.assertEqual(sorted(zip_dict(_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase						)						)		,							_lowerCAmelCase						)





 def        a__				(       self      :						str						)     ->			List[str]:

       """simple docstring"""






       class 						A:



        '''simple docstring'''


        UpperCamelCase		     =		'''bar'''

       lowerCamelCase_	  =					Foo()
       self.assertEqual(foo.my_attr		,							'bar'						)
       with temporary_assignment(_lowerCAmelCase		,							'my_attr'		,							'BAR'						):
             self.assertEqual(foo.my_attr		,							'BAR'						)
       self.assertEqual(foo.my_attr		,							'bar'						)

@pytest.mark.parametrize(
    'iterable_length, num_proc, expected_num_proc'			,							[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]			,							)
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Any			,							lowercase			: Tuple			,							lowercase			: int  ):


      '''simple docstring'''



      with patch('datasets.utils.py_utils._single_map_nested'  ) as mock_single_map_nested, patch(
          'datasets.parallel.parallel.Pool'  ) as mock_multiprocessing_pool:
            lowerCamelCase_	  =					{f"""{i}""": i for i in range(__lowerCAmelCase  )}
            lowerCamelCase_	  =					map_nested(lambda lowercase  : x + 10			,							__lowerCAmelCase			,							num_proc=__lowerCAmelCase			,							parallel_min_length=16  )
            if expected_num_proc == 1:
                  assert mock_single_map_nested.called
                  assert not mock_multiprocessing_pool.called
            else:
                  assert not mock_single_map_nested.called
                  assert mock_multiprocessing_pool.called
                  assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @require_tf
 def        a__				(       self      :						Optional[int]						)     ->			int:

       """simple docstring"""






       import tensorflow as tf
       from tensorflow.keras import layers

       lowerCamelCase_	  =					layers.Dense(2						)

       def gen_random_output():
             lowerCamelCase_	  =					tf.random.uniform((1, 3)						)
             return model(_lowerCAmelCase						).numpy()

       with temp_seed(42		,							set_tensorflow=_lowerCAmelCase						):
             lowerCamelCase_	  =					gen_random_output()
       with temp_seed(42		,							set_tensorflow=_lowerCAmelCase						):
             lowerCamelCase_	  =					gen_random_output()
       lowerCamelCase_	  =					gen_random_output()

       np.testing.assert_equal(_lowerCAmelCase		,							_lowerCAmelCase						)
       self.assertGreater(np.abs(outa - outa						).sum()		,							0						)

 @require_torch
 def        a__				(       self      :						Any						)     ->			int:

       """simple docstring"""






       import torch

       def gen_random_output():
             lowerCamelCase_	  =					torch.nn.Linear(3		,							2						)
             lowerCamelCase_	  =					torch.rand(1		,							3						)
             return model(_lowerCAmelCase						).detach().numpy()

       with temp_seed(42		,							set_pytorch=_lowerCAmelCase						):
             lowerCamelCase_	  =					gen_random_output()
       with temp_seed(42		,							set_pytorch=_lowerCAmelCase						):
             lowerCamelCase_	  =					gen_random_output()
       lowerCamelCase_	  =					gen_random_output()

       np.testing.assert_equal(_lowerCAmelCase		,							_lowerCAmelCase						)
       self.assertGreater(np.abs(outa - outa						).sum()		,							0						)





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

       """simple docstring"""






       def gen_random_output():
             return np.random.rand(1		,							3						)

       with temp_seed(42						):
             lowerCamelCase_	  =					gen_random_output()
       with temp_seed(42						):
             lowerCamelCase_	  =					gen_random_output()
       lowerCamelCase_	  =					gen_random_output()

       np.testing.assert_equal(_lowerCAmelCase		,							_lowerCAmelCase						)
       self.assertGreater(np.abs(outa - outa						).sum()		,							0						)

@pytest.mark.parametrize('input_data'			,							[{}]  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					NestedDataStructure(__lowerCAmelCase  ).data
      assert output_data == input_data

@pytest.mark.parametrize(
    'data, expected_output'			,							[
        ({}, []),
        ([], []),
        ('foo', ['foo']),
        (['foo', 'bar'], ['foo', 'bar']),
        ([['foo', 'bar']], ['foo', 'bar']),
        ([[['foo'], ['bar']]], ['foo', 'bar']),
        ([[['foo'], 'bar']], ['foo', 'bar']),
        ({'a': 1, 'b': 2}, [1, 2]),
        ({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
        ({'a': {'1': 1}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
    ]			,							)
def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					NestedDataStructure(__lowerCAmelCase  ).flatten()
      assert output == expected_output

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					A(x=1			,							y='foobar'  )
      lowerCamelCase_	  =					{'x': 1, 'y': 'foobar'}
      assert asdict(__lowerCAmelCase  ) == expected_output

      lowerCamelCase_	  =					{'a': {'b': A(x=10			,							y='foo'  )}, 'c': [A(x=20			,							y='bar'  )]}
      lowerCamelCase_	  =					{'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
      assert asdict(__lowerCAmelCase  ) == expected_output

      with pytest.raises(__lowerCAmelCase  ):
            asdict([1, A(x=10			,							y='foo'  )]  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      return text.split()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      yield (time.time(), content)
      time.sleep(2  )
      yield (time.time(), content)






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Pool(2  ) as pool:
            lowerCamelCase_	  =					list(iflatmap_unordered(__lowerCAmelCase			,							_split_text			,							kwargs_iterable=[{'text': 'hello there'}] * 10  )  )
            assert out.count('hello'  ) == 10
            assert out.count('there'  ) == 10
            assert len(__lowerCAmelCase  ) == 20

      # check multiprocess from pathos (uses dill for pickling)
      with multiprocess.Pool(2  ) as pool:
            lowerCamelCase_	  =					list(iflatmap_unordered(__lowerCAmelCase			,							_split_text			,							kwargs_iterable=[{'text': 'hello there'}] * 10  )  )
            assert out.count('hello'  ) == 10
            assert out.count('there'  ) == 10
            assert len(__lowerCAmelCase  ) == 20

      # check that we get items as fast as possible
      with Pool(2  ) as pool:
            lowerCamelCase_	  =					[]
            for yield_time, content in iflatmap_unordered(
                __lowerCAmelCase			,							_aseconds_generator_of_aitems_with_timing			,							kwargs_iterable=[{'content': 'a'}, {'content': 'b'}]  ):
                  assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
                  out.append(__lowerCAmelCase  )
            assert out.count('a'  ) == 2
            assert out.count('b'  ) == 2
            assert len(__lowerCAmelCase  ) == 4





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					lbp.get_neighbors_pixel(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase_	  =					np.zeros((image.shape[0], image.shape[1])  )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0			,							image.shape[0]  ):
            for j in range(0			,							image.shape[1]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



