


'''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 _UpperCamelCase       (     __A    )							->		Dict:  # picklable for multiprocessing


      '''simple docstring'''



      return x.sum()







def _UpperCamelCase       (     __A    )							->		int:  # picklable for multiprocessing


      '''simple docstring'''



      return i + 1
@dataclass
class 					lowercase_     :
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	42

class 					lowercase_     (    a__   ):


    def    __a							(     self    ):
          UpperCamelCase__										=				{}
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				1
          UpperCamelCase__										=				[1, 2]
          UpperCamelCase__										=				{"a": 1, "b": 2}
          UpperCamelCase__										=				{"a": [1, 2], "b": [3, 4]}
          UpperCamelCase__										=				{"a": {"1": 1}, "b": 2}
          UpperCamelCase__										=				{"a": 1, "b": 2, "c": 3, "d": 4}
          UpperCamelCase__										=				{}
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				2
          UpperCamelCase__										=				[2, 3]
          UpperCamelCase__										=				{"a": 2, "b": 3}
          UpperCamelCase__										=				{"a": [2, 3], "b": [4, 5]}
          UpperCamelCase__										=				{"a": {"1": 2}, "b": 3}
          UpperCamelCase__										=				{"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    )

          UpperCamelCase__										=				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    )

          UpperCamelCase__										=				{"a": np.eye(2    ), "b": np.zeros(3    ), "c": np.ones(2    )}
          UpperCamelCase__										=				{"a": 2, "b": 0, "c": 2}
          UpperCamelCase__										=				{
              "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    __a							(     self    ):
          UpperCamelCase__										=				{"a": 1, "b": 2}
          UpperCamelCase__										=				{"a": 3, "b": 4}
          UpperCamelCase__										=				{"a": 5, "b": 6}
          UpperCamelCase__										=				sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]    )
          self.assertEqual(sorted(zip_dict(a  ,					a  ,					a    )    )  ,					a    )


    def    __a							(     self    ):
          class 					lowercase_     :
              __UpperCAmelCase   			=	'bar'

          UpperCamelCase__										=				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 _UpperCamelCase       (     __A      ,			__A      ,			__A    )							->		List[Any]:


      '''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:
            UpperCamelCase__										=				{F'''{i}''': i for i in range(__A    )}
            UpperCamelCase__										=				map_nested(lambda __A    : x + 10      ,			__A      ,			num_proc=__A      ,			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 					lowercase_     (    a__   ):


    @require_tf
    def    __a							(     self    ):
          import tensorflow as tf
          from tensorflow.keras import layers

          UpperCamelCase__										=				layers.Dense(2    )

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

          with temp_seed(42  ,					set_tensorflow=a    ):
                UpperCamelCase__										=				gen_random_output()
          with temp_seed(42  ,					set_tensorflow=a    ):
                UpperCamelCase__										=				gen_random_output()
          UpperCamelCase__										=				gen_random_output()

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


    @require_torch
    def    __a							(     self    ):
          import torch

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

          with temp_seed(42  ,					set_pytorch=a    ):
                UpperCamelCase__										=				gen_random_output()
          with temp_seed(42  ,					set_pytorch=a    ):
                UpperCamelCase__										=				gen_random_output()
          UpperCamelCase__										=				gen_random_output()

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


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

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

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







@pytest.mark.parametrize("input_data"      ,			[{}]    )
def _UpperCamelCase       (     __A    )							->		Any:


      '''simple docstring'''



      UpperCamelCase__										=				NestedDataStructure(__A    ).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 _UpperCamelCase       (     __A      ,			__A    )							->		Optional[Any]:


      '''simple docstring'''



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







def _UpperCamelCase       (     )							->		Dict:


      '''simple docstring'''



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

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

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







def _UpperCamelCase       (     __A    )							->		int:


      '''simple docstring'''



      return text.split()







def _UpperCamelCase       (     __A    )							->		List[str]:


      '''simple docstring'''



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







def _UpperCamelCase       (     )							->		int:


      '''simple docstring'''



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

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

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







'''simple docstring'''







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowercase :  Any    							=       (7_2_0, 1_2_8_0)  # Height, Width
_lowercase :  List[Any]    							=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase :  str    							=       1 / 1_0_0
_lowercase :  Any    							=       ""
_lowercase :  Union[str, Any]    							=       ""
_lowercase :  Optional[int]    							=       ""
_lowercase :  List[Any]    							=       2_5_0
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_						, lowercase_	:     Any													=       get_dataset(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						for index in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     str													=       random.sample(range(len(__SCREAMING_SNAKE_CASE							)							)      ,      4							)
												lowercase_						, lowercase_						, lowercase_	:     Any													=       update_image_and_anno(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      filter_scale=__SCREAMING_SNAKE_CASE      ,      )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowercase_	:     int													=       random_chars(32							)
												lowercase_	:     str													=       path.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												lowercase_	:     int													=       F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
												cva.imwrite(F'''{file_root}.jpg'''      ,      __SCREAMING_SNAKE_CASE      ,      [cva.IMWRITE_JPEG_QUALITY, 85]							)
												print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''							)
												lowercase_	:     List[Any]													=       []
												for anno in new_annos:
																		lowercase_	:     List[Any]													=       anno[3] - anno[1]
																		lowercase_	:     List[str]													=       anno[4] - anno[2]
																		lowercase_	:     Dict													=       anno[1] + width / 2
																		lowercase_	:     Dict													=       anno[2] + height / 2
																		lowercase_	:     int													=       F'''{anno[0]} {x_center} {y_center} {width} {height}'''
																		annos_list.append(__SCREAMING_SNAKE_CASE							)
												with open(F'''{file_root}.txt'''      ,      '''w'''							) as outfile:
																		outfile.write('''\n'''.join(line for line in annos_list							)							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []
						lowercase_	:     Optional[Any]													=       []
						for label_file in glob.glob(os.path.join(__SCREAMING_SNAKE_CASE      ,      '''*.txt'''							)							):
												lowercase_	:     int													=       label_file.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												with open(__SCREAMING_SNAKE_CASE							) as in_file:
																		lowercase_	:     List[str]													=       in_file.readlines()
												lowercase_	:     Optional[Any]													=       os.path.join(__SCREAMING_SNAKE_CASE      ,      F'''{label_name}.jpg'''							)

												lowercase_	:     Optional[int]													=       []
												for obj_list in obj_lists:
																		lowercase_	:     List[str]													=       obj_list.rstrip('''\n'''							).split(''' '''							)
																		lowercase_	:     Optional[int]													=       float(obj[1]							) - float(obj[3]							) / 2
																		lowercase_	:     Any													=       float(obj[2]							) - float(obj[4]							) / 2
																		lowercase_	:     str													=       float(obj[1]							) + float(obj[3]							) / 2
																		lowercase_	:     List[str]													=       float(obj[2]							) + float(obj[4]							) / 2

																		boxes.append([int(obj[0]							), xmin, ymin, xmax, ymax]							)
												if not boxes:
																		continue
												img_paths.append(__SCREAMING_SNAKE_CASE							)
												labels.append(__SCREAMING_SNAKE_CASE							)
						return img_paths, labels
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list[int]      ,      __SCREAMING_SNAKE_CASE					: tuple[int, int]      ,      __SCREAMING_SNAKE_CASE					: tuple[float, float]      ,      __SCREAMING_SNAKE_CASE					: float = 0.0      ,      ):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       np.zeros([output_size[0], output_size[1], 3]      ,      dtype=np.uinta							)
						lowercase_	:     Tuple													=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowercase_	:     List[Any]													=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowercase_	:     Optional[int]													=       int(scale_x * output_size[1]							)
						lowercase_	:     Dict													=       int(scale_y * output_size[0]							)

						lowercase_	:     Union[str, Any]													=       []
						lowercase_	:     List[Any]													=       []
						for i, index in enumerate(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     Union[str, Any]													=       all_img_list[index]
												path_list.append(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     int													=       all_annos[index]
												lowercase_	:     Dict													=       cva.imread(__SCREAMING_SNAKE_CASE							)
												if i == 0:  # top-left
																		lowercase_	:     Optional[Any]													=       cva.resize(__SCREAMING_SNAKE_CASE      ,      (divid_point_x, divid_point_y)							)
																		lowercase_	:     Tuple													=       img
																		for bbox in img_annos:
																								lowercase_	:     Optional[int]													=       bbox[1] * scale_x
																								lowercase_	:     Optional[Any]													=       bbox[2] * scale_y
																								lowercase_	:     str													=       bbox[3] * scale_x
																								lowercase_	:     Tuple													=       bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)
												elif i == 1:  # top-right
																		lowercase_	:     Dict													=       cva.resize(__SCREAMING_SNAKE_CASE      ,      (output_size[1] - divid_point_x, divid_point_y)							)
																		lowercase_	:     Dict													=       img
																		for bbox in img_annos:
																								lowercase_	:     int													=       scale_x + bbox[1] * (1 - scale_x)
																								lowercase_	:     Dict													=       bbox[2] * scale_y
																								lowercase_	:     Optional[int]													=       scale_x + bbox[3] * (1 - scale_x)
																								lowercase_	:     int													=       bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)
												elif i == 2:  # bottom-left
																		lowercase_	:     List[Any]													=       cva.resize(__SCREAMING_SNAKE_CASE      ,      (divid_point_x, output_size[0] - divid_point_y)							)
																		lowercase_	:     List[str]													=       img
																		for bbox in img_annos:
																								lowercase_	:     Any													=       bbox[1] * scale_x
																								lowercase_	:     Optional[int]													=       scale_y + bbox[2] * (1 - scale_y)
																								lowercase_	:     str													=       bbox[3] * scale_x
																								lowercase_	:     Optional[int]													=       scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)
												else:  # bottom-right
																		lowercase_	:     int													=       cva.resize(
																		    __SCREAMING_SNAKE_CASE      ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)							)
																		lowercase_	:     List[str]													=       img
																		for bbox in img_annos:
																								lowercase_	:     int													=       scale_x + bbox[1] * (1 - scale_x)
																								lowercase_	:     Any													=       scale_y + bbox[2] * (1 - scale_y)
																								lowercase_	:     Optional[Any]													=       scale_x + bbox[3] * (1 - scale_x)
																								lowercase_	:     int													=       scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowercase_	:     Optional[Any]													=       [
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						assert number_char > 1, "The number of character should greater than 1"
						lowercase_	:     Any													=       ascii_lowercase + digits
						return "".join(random.choice(__SCREAMING_SNAKE_CASE							) for _ in range(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							main()
							print("DONE ✅")



