






'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


SCREAMING_SNAKE_CASE__     =       None
SCREAMING_SNAKE_CASE__     =       '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE__     =       [
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]






@dataclass
class     a_						:
  lowercase							=				True
  lowercase							=				None
  # Automatically constructed
  lowercase							=				"PIL.Image.Image"
  lowercase							=				pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}    )
  lowercase							=				field(default="""Image"""     ,	init=lowerCamelCase     ,	repr=lowerCamelCase    )

  def __call__(				self				)  ->				Dict:



        """simple docstring"""






        return self.pa_type

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				dict:



        """simple docstring"""






        if config.PIL_AVAILABLE:
              import PIL.Image
        else:
              raise ImportError("""To support encoding images, please install 'Pillow'."""				)

        if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = np.array(_SCREAMING_SNAKE_CASE				)

        if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              return {"path": value, "bytes": None}
        elif isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              return {"path": None, "bytes": value}
        elif isinstance(_SCREAMING_SNAKE_CASE      ,		np.ndarray				):
              # convert the image array to PNG/TIFF bytes
              return encode_np_array(_SCREAMING_SNAKE_CASE				)
        elif isinstance(_SCREAMING_SNAKE_CASE      ,		PIL.Image.Image				):
              # convert the PIL image to bytes (default format is PNG/TIFF)
              return encode_pil_image(_SCREAMING_SNAKE_CASE				)
        elif value.get("""path"""				) is not None and os.path.isfile(value["""path"""]				):
              # we set "bytes": None to not duplicate the data if they're already available locally
              return {"bytes": None, "path": value.get("""path"""				)}
        elif value.get("""bytes"""				) is not None or value.get("""path"""				) is not None:
              # store the image bytes, and path is used to infer the image format using the file extension
              return {"bytes": value.get("""bytes"""				), "path": value.get("""path"""				)}
        else:
              raise ValueError(
                  F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None				)  ->				"PIL.Image.Image":



        """simple docstring"""






        if not self.decode:
              raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""				)

        if config.PIL_AVAILABLE:
              import PIL.Image
        else:
              raise ImportError("""To support decoding images, please install 'Pillow'."""				)

        if token_per_repo_id is None:
              UpperCamelCase      = {}

        UpperCamelCase  ,UpperCamelCase      = value["""path"""], value["""bytes"""]
        if bytes_ is None:
              if path is None:
                    raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}."				)
              else:
                    if is_local_path(_SCREAMING_SNAKE_CASE				):
                          UpperCamelCase      = PIL.Image.open(_SCREAMING_SNAKE_CASE				)
                    else:
                          UpperCamelCase      = path.split("""::"""				)[-1]
                          try:
                                UpperCamelCase      = string_to_dict(_SCREAMING_SNAKE_CASE      ,		config.HUB_DATASETS_URL				)["""repo_id"""]
                                UpperCamelCase      = token_per_repo_id.get(_SCREAMING_SNAKE_CASE				)
                          except ValueError:
                                UpperCamelCase      = None
                          with xopen(_SCREAMING_SNAKE_CASE      ,		"""rb"""      ,		use_auth_token=_SCREAMING_SNAKE_CASE				) as f:
                                UpperCamelCase      = BytesIO(f.read()				)
                          UpperCamelCase      = PIL.Image.open(bytes_				)
        else:
              UpperCamelCase      = PIL.Image.open(BytesIO(bytes_				)				)
        image.load()  # to avoid "Too many open files" errors
        return image

  def   A__						(				self				)  ->				Union["FeatureType", Dict[str, "FeatureType"]]:



        """simple docstring"""






        from .features import Value

        return (
            self
            if self.decode
            else {
                "bytes": Value("""binary"""				),
                "path": Value("""string"""				),
            }
        )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				pa.StructArray:



        """simple docstring"""






        if pa.types.is_string(storage.type				):
              UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.binary()				)
              UpperCamelCase      = pa.StructArray.from_arrays([bytes_array, storage]      ,		["""bytes""", """path"""]      ,		mask=storage.is_null()				)
        elif pa.types.is_binary(storage.type				):
              UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.string()				)
              UpperCamelCase      = pa.StructArray.from_arrays([storage, path_array]      ,		["""bytes""", """path"""]      ,		mask=storage.is_null()				)
        elif pa.types.is_struct(storage.type				):
              if storage.type.get_field_index("""bytes"""				) >= 0:
                    UpperCamelCase      = storage.field("""bytes"""				)
              else:
                    UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.binary()				)
              if storage.type.get_field_index("""path"""				) >= 0:
                    UpperCamelCase      = storage.field("""path"""				)
              else:
                    UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.string()				)
              UpperCamelCase      = pa.StructArray.from_arrays([bytes_array, path_array]      ,		["""bytes""", """path"""]      ,		mask=storage.is_null()				)
        elif pa.types.is_list(storage.type				):
              UpperCamelCase      = pa.array(
                  [encode_np_array(np.array(_SCREAMING_SNAKE_CASE				)				)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]      ,		type=pa.binary()      ,		)
              UpperCamelCase      = pa.array([None] * len(_SCREAMING_SNAKE_CASE				)      ,		type=pa.string()				)
              UpperCamelCase      = pa.StructArray.from_arrays(
                  [bytes_array, path_array]      ,		["""bytes""", """path"""]      ,		mask=bytes_array.is_null()				)
        return array_cast(_SCREAMING_SNAKE_CASE      ,		self.pa_type				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				pa.StructArray:



        """simple docstring"""






        @no_op_if_value_is_null
        def path_to_bytes(_SCREAMING_SNAKE_CASE				):
              with xopen(_SCREAMING_SNAKE_CASE      ,		"""rb"""				) as f:
                    UpperCamelCase      = f.read()
              return bytes_

        UpperCamelCase      = pa.array(
            [
                (path_to_bytes(x["""path"""]				) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
                for x in storage.to_pylist()
            ]      ,		type=pa.binary()      ,		)
        UpperCamelCase      = pa.array(
            [os.path.basename(_SCREAMING_SNAKE_CASE				) if path is not None else None for path in storage.field("""path"""				).to_pylist()]      ,		type=pa.string()      ,		)
        UpperCamelCase      = pa.StructArray.from_arrays([bytes_array, path_array]      ,		["""bytes""", """path"""]      ,		mask=bytes_array.is_null()				)
        return array_cast(_SCREAMING_SNAKE_CASE      ,		self.pa_type				)







def    lowercase__			(				)->			List[str]:
      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""   )

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            UpperCamelCase      = list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
      return _IMAGE_COMPRESSION_FORMATS







def    lowercase__			(				__UpperCamelCase   )->			bytes:
      UpperCamelCase      = BytesIO()
      if image.format in list_image_compression_formats():
            UpperCamelCase      = image.format
      else:
            UpperCamelCase      = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
      image.save(__UpperCamelCase		,						format=__UpperCamelCase   )
      return buffer.getvalue()







def    lowercase__			(				__UpperCamelCase   )->			dict:
      if hasattr(__UpperCamelCase		,						"""filename"""   ) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(__UpperCamelCase   )}







def    lowercase__			(				__UpperCamelCase   )->			dict:
      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""   )

      UpperCamelCase      = array.dtype
      UpperCamelCase      = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
      UpperCamelCase      = dtype.kind
      UpperCamelCase      = dtype.itemsize

      UpperCamelCase      = None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            UpperCamelCase      = np.dtype("""|u1"""   )
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      F"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."   )
            if dtype is not dest_dtype:
                  warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"   )
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            UpperCamelCase      = dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  UpperCamelCase      = dtype_byteorder + dtype_kind + str(__UpperCamelCase   )
                  UpperCamelCase      = np.dtype(__UpperCamelCase   )
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"   )
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                F"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"   )
      UpperCamelCase      = PIL.Image.fromarray(array.astype(__UpperCamelCase   )   )
      return {"path": None, "bytes": image_to_bytes(__UpperCamelCase   )}







def    lowercase__			(				__UpperCamelCase   )->			List[dict]:
      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""   )

      if objs:
            UpperCamelCase  ,UpperCamelCase      = first_non_null_value(__UpperCamelCase   )
            if isinstance(__UpperCamelCase		,						__UpperCamelCase   ):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(__UpperCamelCase		,						np.ndarray   ):
                  UpperCamelCase      = no_op_if_value_is_null(__UpperCamelCase   )
                  return [obj_to_image_dict_func(__UpperCamelCase   ) for obj in objs]
            elif isinstance(__UpperCamelCase		,						PIL.Image.Image   ):
                  UpperCamelCase      = no_op_if_value_is_null(__UpperCamelCase   )
                  return [obj_to_image_dict_func(__UpperCamelCase   ) for obj in objs]
            else:
                  return objs
      else:
            return objs








'''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    lowercase__			(				__UpperCamelCase   )->			Dict:  # picklable for multiprocessing
      return x.sum()







def    lowercase__			(				__UpperCamelCase   )->			Tuple:  # picklable for multiprocessing
      return i + 1






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42






class     a_						(      lowerCamelCase    ):

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



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 2
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

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

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)      ,		_SCREAMING_SNAKE_CASE				)


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



        """simple docstring"""






        class     a_						:
          lowercase							=				"""bar"""


        UpperCamelCase      = Foo()
        self.assertEqual(foo.my_attr      ,		"""bar"""				)
        with temporary_assignment(_SCREAMING_SNAKE_CASE      ,		"""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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      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(__UpperCamelCase   )}
            UpperCamelCase      = 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     a_						(      lowerCamelCase    ):

  @require_tf
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE				).numpy()

        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

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

  @require_torch
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        import torch

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

        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

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


  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertGreater(np.abs(outa - outa				).sum()      ,		0				)







@pytest.mark.parametrize("""input_data"""		,						[{}]   )
def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = 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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = NestedDataStructure(__UpperCamelCase   ).flatten()
      assert output == expected_output







def    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = A(x=1		,						y="""foobar"""   )
      UpperCamelCase      = {"""x""": 1, """y""": """foobar"""}
      assert asdict(__UpperCamelCase   ) == 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(__UpperCamelCase   ) == expected_output

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







def    lowercase__			(				__UpperCamelCase   )->			List[Any]:
      return text.split()







def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      yield (time.time(), content)
      time.sleep(2   )
      yield (time.time(), content)







def    lowercase__			(				)->			int:
      with Pool(2   ) as pool:
            UpperCamelCase      = 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:
            UpperCamelCase      = 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:
            UpperCamelCase      = []
            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
