


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   (      A__						: Optional[int]					)     ->    int:  # picklable for multiprocessing
   return x.sum()


def    __lowerCamelCase   (      A__						: int					)     ->    Optional[int]:  # picklable for multiprocessing
   return i + 1





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							42
       _a   							=							42







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       def 				_lowerCAmelCase							(						self				:				Dict							)					->List[str]:
          lowerCamelCase_						:   List[str]		= {}
          lowerCamelCase_						:   Union[str, Any]		= []
          lowerCamelCase_						:   Optional[Any]		= 1
          lowerCamelCase_						:   List[Any]		= [1, 2]
          lowerCamelCase_						:   Any		= {"""a""": 1, """b""": 2}
          lowerCamelCase_						:   Tuple		= {"""a""": [1, 2], """b""": [3, 4]}
          lowerCamelCase_						:   List[str]		= {"""a""": {"""1""": 1}, """b""": 2}
          lowerCamelCase_						:   Union[str, Any]		= {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
          lowerCamelCase_						:   List[str]		= {}
          lowerCamelCase_						:   Dict		= []
          lowerCamelCase_						:   List[Any]		= 2
          lowerCamelCase_						:   Optional[int]		= [2, 3]
          lowerCamelCase_						:   Union[str, Any]		= {"""a""": 2, """b""": 3}
          lowerCamelCase_						:   Any		= {"""a""": [2, 3], """b""": [4, 5]}
          lowerCamelCase_						:   Dict		= {"""a""": {"""1""": 2}, """b""": 3}
          lowerCamelCase_						:   Dict		= {"""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							)

          lowerCamelCase_						:   List[str]		= 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							)

          lowerCamelCase_						:   Optional[Any]		= {"""a""": np.eye(2							), """b""": np.zeros(3							), """c""": np.ones(2							)}
          lowerCamelCase_						:   Any		= {"""a""": 2, """b""": 0, """c""": 2}
          lowerCamelCase_						:   Tuple		= {
              """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 				_lowerCAmelCase							(						self				:				str							)					->List[Any]:
          lowerCamelCase_						:   Tuple		= {"""a""": 1, """b""": 2}
          lowerCamelCase_						:   List[str]		= {"""a""": 3, """b""": 4}
          lowerCamelCase_						:   List[Any]		= {"""a""": 5, """b""": 6}
          lowerCamelCase_						:   Any		= sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]							)
          self.assertEqual(sorted(zip_dict(__a		,				__a		,				__a							)							)		,				__a							)




       def 				_lowerCAmelCase							(						self				:				Dict							)					->str:





          class   SCREAMING_SNAKE_CASE_			:




                 '''simple docstring'''



                 _a   							=							"bar"







          lowerCamelCase_						:   Any		= 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   (      A__						: str						,						A__						: List[Any]						,						A__						: Optional[Any]					)     ->    int:
   with patch("""datasets.utils.py_utils._single_map_nested"""					) as mock_single_map_nested, patch(
       """datasets.parallel.parallel.Pool"""					) as mock_multiprocessing_pool:
      lowerCamelCase_						:   Dict		= {f'''{i}''': i for i in range(A__					)}
      lowerCamelCase_						:   Union[str, Any]		= 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   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       @require_tf
       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Dict:
          import tensorflow as tf
          from tensorflow.keras import layers

          lowerCamelCase_						:   List[str]		= layers.Dense(2							)

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

          with temp_seed(42		,				set_tensorflow=__a							):
             lowerCamelCase_						:   int		= gen_random_output()
          with temp_seed(42		,				set_tensorflow=__a							):
             lowerCamelCase_						:   List[str]		= gen_random_output()
          lowerCamelCase_						:   Union[str, Any]		= gen_random_output()

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



       @require_torch
       def 				_lowerCAmelCase							(						self				:				List[str]							)					->List[str]:
          import torch

          def gen_random_output():
             lowerCamelCase_						:   Optional[Any]		= torch.nn.Linear(3		,				2							)
             lowerCamelCase_						:   str		= torch.rand(1		,				3							)
             return model(__a							).detach().numpy()

          with temp_seed(42		,				set_pytorch=__a							):
             lowerCamelCase_						:   int		= gen_random_output()
          with temp_seed(42		,				set_pytorch=__a							):
             lowerCamelCase_						:   Union[str, Any]		= gen_random_output()
          lowerCamelCase_						:   Tuple		= gen_random_output()

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




       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->List[Any]:
          def gen_random_output():
             return np.random.rand(1		,				3							)

          with temp_seed(42							):
             lowerCamelCase_						:   int		= gen_random_output()
          with temp_seed(42							):
             lowerCamelCase_						:   Any		= gen_random_output()
          lowerCamelCase_						:   Tuple		= gen_random_output()

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







@pytest.mark.parametrize("""input_data"""						,						[{}]					)
def    __lowerCamelCase   (      A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   Optional[int]		= 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    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: int					)     ->    Optional[Any]:
   lowerCamelCase_						:   int		= NestedDataStructure(A__					).flatten()
   assert output == expected_output


def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   Any		= A(x=1						,						y="""foobar"""					)
   lowerCamelCase_						:   List[Any]		= {"""x""": 1, """y""": """foobar"""}
   assert asdict(A__					) == expected_output

   lowerCamelCase_						:   Tuple		= {"""a""": {"""b""": A(x=10						,						y="""foo"""					)}, """c""": [A(x=20						,						y="""bar"""					)]}
   lowerCamelCase_						:   Dict		= {"""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    __lowerCamelCase   (      A__						: str					)     ->    Any:
   return text.split()


def    __lowerCamelCase   (      A__						: str					)     ->    List[Any]:
   yield (time.time(), content)
   time.sleep(2					)
   yield (time.time(), content)



def    __lowerCamelCase   (      )     ->    List[str]:
   with Pool(2					) as pool:
      lowerCamelCase_						:   Dict		= 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:
      lowerCamelCase_						:   List[str]		= 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:
      lowerCamelCase_						:   Optional[int]		= []
      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









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__				:	Tuple					       =					logging.get_logger(__name__)


if is_vision_available():
      import PIL





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["pixel_values"]



       def __init__(						self				:				List[str]		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				bool = True		,				__a				:				Union[int, float] = 1 / 255		,				__a				:				bool = True		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = True		,				**__a				:				str		,				)					->None:
          super().__init__(**__a							)
          lowerCamelCase_						:   Any		= size if size is not None else {"""shortest_edge""": 224}
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          lowerCamelCase_						:   Any		= get_size_dict(__a		,				default_to_square=__a		,				param_name="""crop_size"""							)

          lowerCamelCase_						:   List[Any]		= do_resize
          lowerCamelCase_						:   str		= size
          lowerCamelCase_						:   Any		= resample
          lowerCamelCase_						:   List[Any]		= do_center_crop
          lowerCamelCase_						:   Any		= crop_size
          lowerCamelCase_						:   List[Any]		= do_rescale
          lowerCamelCase_						:   Tuple		= rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize
          lowerCamelCase_						:   Any		= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          lowerCamelCase_						:   int		= image_std if image_std is not None else OPENAI_CLIP_STD
          lowerCamelCase_						:   Tuple		= do_convert_rgb



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->np.ndarray:
          lowerCamelCase_						:   Optional[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          if "shortest_edge" not in size:
             raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
          lowerCamelCase_						:   Dict		= get_resize_output_image_size(__a		,				size=size["""shortest_edge"""]		,				default_to_square=__a							)
          return resize(__a		,				size=__a		,				resample=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[Any]		,				)					->np.ndarray:
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a							)
          if "height" not in size or "width" not in size:
             raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
          return center_crop(__a		,				size=(size["""height"""], size["""width"""])		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				np.ndarray		,				__a				:				Union[int, float]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->Dict:
          return rescale(__a		,				scale=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				np.ndarray		,				__a				:				Union[float, List[float]]		,				__a				:				Union[float, List[float]]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[str]		,				)					->np.ndarray:
          return normalize(__a		,				mean=__a		,				std=__a		,				data_format=__a		,				**__a							)




       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				ImageInput		,				__a				:				bool = None		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = None		,				__a				:				bool = None		,				__a				:				int = None		,				__a				:				bool = None		,				__a				:				float = None		,				__a				:				bool = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = None		,				__a				:				Optional[Union[str, TensorType]] = None		,				__a				:				Optional[ChannelDimension] = ChannelDimension.FIRST		,				**__a				:				List[str]		,				)					->PIL.Image.Image:
          lowerCamelCase_						:   List[Any]		= do_resize if do_resize is not None else self.do_resize
          lowerCamelCase_						:   Any		= size if size is not None else self.size
          lowerCamelCase_						:   str		= get_size_dict(__a		,				param_name="""size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= resample if resample is not None else self.resample
          lowerCamelCase_						:   List[str]		= do_center_crop if do_center_crop is not None else self.do_center_crop
          lowerCamelCase_						:   List[Any]		= crop_size if crop_size is not None else self.crop_size
          lowerCamelCase_						:   Optional[int]		= get_size_dict(__a		,				param_name="""crop_size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[Any]		= do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase_						:   List[str]		= rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize if do_normalize is not None else self.do_normalize
          lowerCamelCase_						:   Dict		= image_mean if image_mean is not None else self.image_mean
          lowerCamelCase_						:   Tuple		= image_std if image_std is not None else self.image_std
          lowerCamelCase_						:   Union[str, Any]		= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowerCamelCase_						:   Union[str, Any]		= make_list_of_images(__a							)

          if not valid_images(__a							):
             raise ValueError(
                 """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                 """torch.Tensor, tf.Tensor or jax.ndarray."""							)

          if do_resize and size is None:
             raise ValueError("""Size must be specified if do_resize is True."""							)

          if do_center_crop and crop_size is None:
             raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

          if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
             lowerCamelCase_						:   str		= [convert_to_rgb(__a							) for image in images]

          # All transformations expect numpy arrays.
          lowerCamelCase_						:   str		= [to_numpy_array(__a							) for image in images]

          if do_resize:
             lowerCamelCase_						:   int		= [self.resize(image=__a		,				size=__a		,				resample=__a							) for image in images]

          if do_center_crop:
             lowerCamelCase_						:   Dict		= [self.center_crop(image=__a		,				size=__a							) for image in images]

          if do_rescale:
             lowerCamelCase_						:   List[str]		= [self.rescale(image=__a		,				scale=__a							) for image in images]

          if do_normalize:
             lowerCamelCase_						:   Tuple		= [self.normalize(image=__a		,				mean=__a		,				std=__a							) for image in images]

          lowerCamelCase_						:   Tuple		= [to_channel_dimension_format(__a		,				__a							) for image in images]

          lowerCamelCase_						:   Tuple		= {"""pixel_values""": images}
          return BatchFeature(data=__a		,				tensor_type=__a							)





