


"""simple docstring"""

import cva
import numpy as np





class  lowerCamelCase__    :
      def __init__(		self						:						Optional[Any]   ,							A_						:						float   ,							A_						:						int				):






       '''simple docstring'''


       if k in (0.04, 0.06):
        __lowercase						=						k
        __lowercase						=						window_size
       else:
        raise ValueError("""invalid k value"""				)
      def __str__(		self						:						Optional[int]				):






       '''simple docstring'''


       return str(self.k				)






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						str				):






       '''simple docstring'''


       __lowercase						=						cva.imread(A_   ,							0				)
       __lowercase				,		__lowercase						=						img.shape
       __lowercase						=						[]
       __lowercase						=						img.copy()
       __lowercase						=						cva.cvtColor(A_   ,							cva.COLOR_GRAY2RGB				)
       __lowercase				,		__lowercase						=						np.gradient(A_				)
       __lowercase						=						dx**2
       __lowercase						=						dy**2
       __lowercase						=						dx * dy
       __lowercase						=						0.04
       __lowercase						=						self.window_size // 2
       for y in range(A_   ,							h - offset				):
        for x in range(A_   ,							w - offset				):
         __lowercase						=						ixx[
             y - offset : y + offset + 1, x - offset : x + offset + 1
         ].sum()
         __lowercase						=						iyy[
             y - offset : y + offset + 1, x - offset : x + offset + 1
         ].sum()
         __lowercase						=						ixy[
             y - offset : y + offset + 1, x - offset : x + offset + 1
         ].sum()

         __lowercase						=						(wxx * wyy) - (wxy**2)
         __lowercase						=						wxx + wyy
         __lowercase						=						det - k * (trace**2)
         # Can change the value
         if r > 0.5:
          corner_list.append([x, y, r]				)
          color_img.itemset((y, x, 0)   ,							0				)
          color_img.itemset((y, x, 1)   ,							0				)
          color_img.itemset((y, x, 2)   ,							2_5_5				)
       return color_img, corner_list


if __name__ == "__main__":
       UpperCAmelCase__					=HarrisCorner(0.04, 3)
       UpperCAmelCase__				,   UpperCAmelCase__					=edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)




"""simple docstring"""

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 lowerCAmelCase_   (					UpperCamelCase__						:			int			):  # picklable for multiprocessing






 """simple docstring"""







 return x.sum()


def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]			):  # picklable for multiprocessing






 """simple docstring"""







 return i + 1





@dataclass
class  lowerCamelCase__    :
      a			:  int
      a			:  str






class  lowerCamelCase__    (		_a     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						{}
       __lowercase						=						[]
       __lowercase						=						1
       __lowercase						=						[1, 2]
       __lowercase						=						{"""a""": 1, """b""": 2}
       __lowercase						=						{"""a""": [1, 2], """b""": [3, 4]}
       __lowercase						=						{"""a""": {"""1""": 1}, """b""": 2}
       __lowercase						=						{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
       __lowercase						=						{}
       __lowercase						=						[]
       __lowercase						=						2
       __lowercase						=						[2, 3]
       __lowercase						=						{"""a""": 2, """b""": 3}
       __lowercase						=						{"""a""": [2, 3], """b""": [4, 5]}
       __lowercase						=						{"""a""": {"""1""": 2}, """b""": 3}
       __lowercase						=						{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)

       __lowercase						=						2
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)

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






       '''simple docstring'''


       __lowercase						=						{"""a""": 1, """b""": 2}
       __lowercase						=						{"""a""": 3, """b""": 4}
       __lowercase						=						{"""a""": 5, """b""": 6}
       __lowercase						=						sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]				)
       self.assertEqual(sorted(zip_dict(A_   ,							A_   ,							A_				)				)   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       class  lowerCamelCase__    :
             a			:  int           =		"""bar"""






       __lowercase						=						Foo()
       self.assertEqual(foo.my_attr   ,							"""bar"""				)
       with temporary_assignment(A_   ,							"""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 lowerCAmelCase_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			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:
  __lowercase						=						{f'''{i}''': i for i in range(UpperCamelCase__			)}
  __lowercase						=						map_nested(lambda UpperCamelCase__			: x + 10					, UpperCamelCase__					, num_proc=UpperCamelCase__					, 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  lowerCamelCase__    (		_a     ):
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       import tensorflow as tf
       from tensorflow.keras import layers

       __lowercase						=						layers.Dense(2				)

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

       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       import torch

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

       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


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

       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






@pytest.mark.parametrize("""input_data"""					, [{}]			)
def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).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 lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).flatten()
 assert output == expected_output


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						A(x=1					, y="""foobar"""			)
 __lowercase						=						{"""x""": 1, """y""": """foobar"""}
 assert asdict(UpperCamelCase__			) == expected_output

 __lowercase						=						{"""a""": {"""b""": A(x=10					, y="""foo"""			)}, """c""": [A(x=20					, y="""bar"""			)]}
 __lowercase						=						{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
 assert asdict(UpperCamelCase__			) == expected_output

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


def lowerCAmelCase_   (					UpperCamelCase__						:			str			):






 """simple docstring"""







 return text.split()


def lowerCAmelCase_   (					UpperCamelCase__						:			Dict			):






 """simple docstring"""







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


def lowerCAmelCase_   (					):






 """simple docstring"""







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

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

 # check that we get items as fast as possible
 with Pool(2			) as pool:
  __lowercase						=						[]
  for yield_time, content in iflatmap_unordered(
      UpperCamelCase__					, _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(UpperCamelCase__			)
  assert out.count("""a"""			) == 2
  assert out.count("""b"""			) == 2
  assert len(UpperCamelCase__			) == 4
