'''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 __a (	__lowerCamelCase			:						List[str]				)	->							List[Any]:  # picklable for multiprocessing
      '''simple docstring'''


      return x.sum()


def __a (	__lowerCamelCase			:						Dict				)	->							Union[str, Any]:  # picklable for multiprocessing
      '''simple docstring'''


      return i + 1





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=42
   lowerCamelCase_    					=42





class 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					Optional[Any]:
         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(__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)

         lowercase_     =      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)

         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(__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 __lowerCAmelCase: x + 1   ,		__lowerCAmelCase   ,		num_proc=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Dict:
         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(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase))   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:





         class 							lowercase						:
            lowerCamelCase_    					='bar'




         lowercase_     =      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 __a (	__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Dict				)	->							Tuple:
      '''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(__lowerCamelCase				)}
            lowercase_     =      map_nested(lambda __lowerCamelCase				: 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 							lowercase						(						__lowerCamelCase ):


   @require_tf
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Optional[int]:
         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(__lowerCAmelCase).numpy()

         with temp_seed(42   ,		set_tensorflow=__lowerCAmelCase):
               lowercase_     =      gen_random_output()
         with temp_seed(42   ,		set_tensorflow=__lowerCAmelCase):
               lowercase_     =      gen_random_output()
         lowercase_     =      gen_random_output()

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


   @require_torch
   def __UpperCAmelCase       (   self				:	List[Any])      ->					Union[str, Any]:
         import torch

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

         with temp_seed(42   ,		set_pytorch=__lowerCAmelCase):
               lowercase_     =      gen_random_output()
         with temp_seed(42   ,		set_pytorch=__lowerCAmelCase):
               lowercase_     =      gen_random_output()
         lowercase_     =      gen_random_output()

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					Optional[int]:
         def gen_random_output():
               return np.random.rand(1   ,		3)

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

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




@pytest.mark.parametrize("input_data" ,		[{}]				)
def __a (	__lowerCamelCase			:						Union[str, Any]				)	->							Optional[int]:
      '''simple docstring'''


      lowercase_     =      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 __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						str				)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      NestedDataStructure(__lowerCamelCase				).flatten()
      assert output == expected_output


def __a (	)	->							Optional[Any]:
      '''simple docstring'''


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

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


def __a (	__lowerCamelCase			:						str				)	->							int:
      '''simple docstring'''


      return text.split()


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							Dict:
      '''simple docstring'''


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






def __a (	)	->							Tuple:
      '''simple docstring'''


      with Pool(2				) as pool:
            lowercase_     =      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:
            lowercase_     =      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:
            lowercase_     =      []
            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







'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	List[str]=PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					None:
         lowercase_     =      do_resize
         lowercase_     =      do_rescale
         lowercase_     =      size_divisor
         lowercase_     =      resample
         super().__init__(**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	Any)      ->					np.ndarray:
         lowercase_ ,							lowercase_     =      get_image_size(__lowerCAmelCase)
         # Rounds the height and width down to the closest multiple of size_divisor
         lowercase_     =      height // size_divisor * size_divisor
         lowercase_     =      width // size_divisor * size_divisor
         lowercase_     =      resize(__lowerCAmelCase   ,		(new_h, new_w)   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)
         return image


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	float   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	int)      ->					np.ndarray:
         return rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	str=None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[TensorType, str]] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					BatchFeature:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      size_divisor if size_divisor is not None else self.size_divisor
         lowercase_     =      resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
               raise ValueError("size_divisor is required for resizing")

         lowercase_     =      make_list_of_images(__lowerCAmelCase)

         if not valid_images(__lowerCAmelCase):
               raise ValueError("Invalid image(s)")

         # All transformations expect numpy arrays.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for img in images]

         if do_resize:
               lowercase_     =      [self.resize(__lowerCAmelCase   ,		size_divisor=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(__lowerCAmelCase   ,		scale=1 / 255) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)






