import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class 			lowerCamelCase_  :






    '''simple docstring'''






    def __init__(			self				,				__lowercase = "cpu"				,				__lowercase = "openai/clip-vit-large-patch14") ->			List[Any]:
        __UpperCamelCase  :Optional[int]    						=	device
        __UpperCamelCase  :str    						=	CLIPTokenizerFast.from_pretrained(a_)
        __UpperCamelCase  :Union[str, Any]    						=	[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
        __UpperCamelCase  :Optional[int]    						=	[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
        __UpperCamelCase  :str    						=	torchvision.transforms.Normalize(self.image_mean				,				self.image_std)
        __UpperCamelCase  :Optional[int]    						=	torchvision.transforms.Resize(224)
        __UpperCamelCase  :str    						=	torchvision.transforms.CenterCrop(224)







    def     UpperCamelCase__     (			self				,				__lowercase) ->			List[Any]:
        __UpperCamelCase  :Optional[int]    						=	self.resize(a_)
        __UpperCamelCase  :List[Any]    						=	self.center_crop(a_)
        __UpperCamelCase  :Optional[Any]    						=	self.normalize(a_)
        return images







    def __call__(			self				,				__lowercase=None				,				__lowercase=None				,				**__lowercase) ->			int:
        __UpperCamelCase  :Optional[int]    						=	self.tokenizer(text=a_				,				**a_)
        __UpperCamelCase  :Any    						=	self.preprocess_img(a_)
        __UpperCamelCase  :Union[str, Any]    						=	{key: value.to(self.device) for (key, value) in encoding.items()}
        return encoding


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase=10				,				__lowercase=0.01				,				__lowercase=None				,				__lowercase=None				,				__lowercase=None				,				__lowercase=None				,				__lowercase=None				,				__lowercase=None				,				__lowercase=False				,				__lowercase=True				,				__lowercase="image"				,				__lowercase=True				,				__lowercase=False				,				__lowercase=False				,				__lowercase=False				,				) ->			Any:
        super().__init__()
        __UpperCamelCase  :int    						=	None
        __UpperCamelCase  :List[str]    						=	device if device else get_device()
        if vqgan:
            __UpperCamelCase  :Any    						=	vqgan
        else:
            __UpperCamelCase  :Optional[Any]    						=	load_vqgan(self.device				,				conf_path=a_				,				ckpt_path=a_)
        self.vqgan.eval()
        if clip:
            __UpperCamelCase  :Tuple    						=	clip
        else:
            __UpperCamelCase  :Optional[Any]    						=	CLIPModel.from_pretrained('''openai/clip-vit-base-patch32''')
        self.clip.to(self.device)
        __UpperCamelCase  :List[str]    						=	ProcessorGradientFlow(device=self.device)

        __UpperCamelCase  :Union[str, Any]    						=	iterations
        __UpperCamelCase  :Dict    						=	lr
        __UpperCamelCase  :Optional[int]    						=	log
        __UpperCamelCase  :List[str]    						=	make_grid
        __UpperCamelCase  :Union[str, Any]    						=	return_val
        __UpperCamelCase  :List[str]    						=	quantize
        __UpperCamelCase  :List[str]    						=	self.vqgan.decoder.z_shape







    def     UpperCamelCase__     (			self				,				__lowercase=None				,				__lowercase=None				,				__lowercase=5				,				__lowercase=True) ->			int:
        __UpperCamelCase  :Dict    						=	[]
        if output_path is None:
            __UpperCamelCase  :Tuple    						=	"""./animation.gif"""
        if input_path is None:
            __UpperCamelCase  :Any    						=	self.save_path
        __UpperCamelCase  :Optional[int]    						=	sorted(glob(input_path + '''/*'''))
        if not len(a_):
            raise ValueError(
                '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
                ''' function?)''')
        if len(a_) == 1:
            print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)''')
        __UpperCamelCase  :List[Any]    						=	total_duration / len(a_)
        __UpperCamelCase  :Optional[Any]    						=	[frame_duration] * len(a_)
        if extend_frames:
            __UpperCamelCase  :Optional[int]    						=	1.5
            __UpperCamelCase  :int    						=	3
        for file_name in paths:
            if file_name.endswith('''.png'''):
                images.append(imageio.imread(a_))
        imageio.mimsave(a_				,				a_				,				duration=a_)
        print(f"""gif saved to {output_path}""")







    def     UpperCamelCase__     (			self				,				__lowercase=None				,				__lowercase=None) ->			List[str]:



        if not (path or img):
            raise ValueError('''Input either path or tensor''')
        if img is not None:
            raise NotImplementedError
        __UpperCamelCase  :int    						=	preprocess(Image.open(a_)				,				target_image_size=256).to(self.device)
        __UpperCamelCase  :int    						=	preprocess_vqgan(a_)
        __UpperCamelCase  :List[Any]    						=	self.vqgan.encode(a_)
        return z







    def     UpperCamelCase__     (			self				,				__lowercase) ->			str:
        __UpperCamelCase  :Optional[int]    						=	self.latent.detach().requires_grad_()
        __UpperCamelCase  :Tuple    						=	base_latent + transform_vector
        if self.quantize:
            __UpperCamelCase  :Any    						=	self.vqgan.quantize(a_)
        else:
            __UpperCamelCase  :List[Any]    						=	trans_latent
        return self.vqgan.decode(a_)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase=None) ->			str:
        __UpperCamelCase  :Tuple    						=	self.clip_preprocessor(text=a_				,				images=a_				,				return_tensors='''pt'''				,				padding=a_)
        __UpperCamelCase  :Any    						=	self.clip(**a_)
        __UpperCamelCase  :str    						=	clip_outputs.logits_per_image
        if weights is not None:
            __UpperCamelCase  :Any    						=	similarity_logits * weights
        return similarity_logits.sum()







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	self._get_clip_similarity(pos_prompts['''prompts''']				,				a_				,				weights=(1 / pos_prompts['''weights''']))
        if neg_prompts:
            __UpperCamelCase  :List[str]    						=	self._get_clip_similarity(neg_prompts['''prompts''']				,				a_				,				weights=neg_prompts['''weights'''])
        else:
            __UpperCamelCase  :Tuple    						=	torch.tensor([1]				,				device=self.device)
        __UpperCamelCase  :int    						=	-torch.log(a_) + torch.log(a_)
        return loss







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			Any:
        __UpperCamelCase  :Tuple    						=	torch.randn_like(self.latent				,				requires_grad=a_				,				device=self.device)
        __UpperCamelCase  :Dict    						=	torch.optim.Adam([vector]				,				lr=self.lr)

        for i in range(self.iterations):
            optim.zero_grad()
            __UpperCamelCase  :str    						=	self._add_vector(a_)
            __UpperCamelCase  :List[Any]    						=	loop_post_process(a_)
            __UpperCamelCase  :List[Any]    						=	self._get_CLIP_loss(a_				,				a_				,				a_)
            print('''CLIP loss'''				,				a_)
            if self.log:
                wandb.log({'''CLIP Loss''': clip_loss})
            clip_loss.backward(retain_graph=a_)
            optim.step()
            if self.return_val == "image":
                yield custom_to_pil(transformed_img[0])
            else:
                yield vector







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			Dict:
        wandb.init(reinit=a_				,				project='''face-editor''')
        wandb.config.update({'''Positive Prompts''': positive_prompts})
        wandb.config.update({'''Negative Prompts''': negative_prompts})
        wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations})
        if image_path:
            __UpperCamelCase  :Any    						=	Image.open(a_)
            __UpperCamelCase  :str    						=	image.resize((256, 256))
            wandb.log('''Original Image'''				,				wandb.Image(a_))







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Optional[int]:



        if not prompts:
            return []
        __UpperCamelCase  :List[str]    						=	[]
        __UpperCamelCase  :Tuple    						=	[]
        if isinstance(a_				,				a_):
            __UpperCamelCase  :Union[str, Any]    						=	[prompt.strip() for prompt in prompts.split('''|''')]
        for prompt in prompts:
            if isinstance(a_				,				(tuple, list)):
                __UpperCamelCase  :List[Any]    						=	prompt[0]
                __UpperCamelCase  :Optional[Any]    						=	float(prompt[1])
            elif ":" in prompt:
                __UpperCamelCase  :List[Any]    						=	prompt.split(''':''')
                __UpperCamelCase  :str    						=	float(a_)
            else:
                __UpperCamelCase  :int    						=	prompt
                __UpperCamelCase  :Union[str, Any]    						=	1.0
            processed_prompts.append(a_)
            weights.append(a_)
        return {
            "prompts": processed_prompts,
            "weights": torch.tensor(a_				,				device=self.device),
        }







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=None				,				__lowercase=None				,				__lowercase=True				,				__lowercase=False				,				__lowercase=True				,				__lowercase=True				,				__lowercase=None				,				) ->			List[str]:



        if image_path:
            __UpperCamelCase  :Union[str, Any]    						=	self._get_latent(a_)
        else:
            __UpperCamelCase  :Any    						=	torch.randn(self.latent_dim				,				device=self.device)
        if self.log:
            self._init_logging(a_				,				a_				,				a_)

        assert pos_prompts, "You must provide at least one positive prompt."
        __UpperCamelCase  :str    						=	self.process_prompts(a_)
        __UpperCamelCase  :Dict    						=	self.process_prompts(a_)
        if save_final and save_path is None:
            __UpperCamelCase  :Any    						=	os.path.join('''./outputs/'''				,				'''_'''.join(pos_prompts['''prompts''']))
        if not os.path.exists(a_):
            os.makedirs(a_)
        else:
            __UpperCamelCase  :List[Any]    						=	save_path + """_""" + get_timestamp()
            os.makedirs(a_)
        __UpperCamelCase  :Optional[Any]    						=	save_path

        __UpperCamelCase  :List[Any]    						=	self.vqgan.decode(self.latent)[0]
        if show_intermediate:
            print('''Original Image''')
            show_pil(custom_to_pil(a_))

        __UpperCamelCase  :List[Any]    						=	loop_post_process(a_)
        for iter, transformed_img in enumerate(self._optimize_CLIP(a_				,				a_				,				a_)):
            if show_intermediate:
                show_pil(a_)
            if save_intermediate:
                transformed_img.save(os.path.join(self.save_path				,				f"""iter_{iter:03d}.png"""))
            if self.log:
                wandb.log({'''Image''': wandb.Image(a_)})
        if show_final:
            show_pil(a_)
        if save_final:
            transformed_img.save(os.path.join(self.save_path				,				f"""iter_{iter:03d}_final.png"""))






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						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return x.sum()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return i + 1


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		int
    a__			:		str


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Dict    						=	{}
        __UpperCamelCase  :int    						=	[]
        __UpperCamelCase  :int    						=	1
        __UpperCamelCase  :int    						=	[1, 2]
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :Dict    						=	{'''a''': [1, 2], '''b''': [3, 4]}
        __UpperCamelCase  :Optional[int]    						=	{'''a''': {'''1''': 1}, '''b''': 2}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
        __UpperCamelCase  :str    						=	{}
        __UpperCamelCase  :Any    						=	[]
        __UpperCamelCase  :Optional[Any]    						=	2
        __UpperCamelCase  :Optional[int]    						=	[2, 3]
        __UpperCamelCase  :List[Any]    						=	{'''a''': 2, '''b''': 3}
        __UpperCamelCase  :List[str]    						=	{'''a''': [2, 3], '''b''': [4, 5]}
        __UpperCamelCase  :int    						=	{'''a''': {'''1''': 2}, '''b''': 3}
        __UpperCamelCase  :Union[str, Any]    						=	{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	2
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)

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







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :List[str]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :int    						=	{'''a''': 3, '''b''': 4}
        __UpperCamelCase  :List[str]    						=	{'''a''': 5, '''b''': 6}
        __UpperCamelCase  :List[Any]    						=	sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))])
        self.assertEqual(sorted(zip_dict(__lowercase				,				__lowercase				,				__lowercase))				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:


        class 			lowerCamelCase_  :






            '''simple docstring'''






            a__			:		int      			=   """bar"""
        __UpperCamelCase  :List[str]    						=	Foo()
        self.assertEqual(foo.my_attr				,				'''bar''')
        with temporary_assignment(__lowercase				,				'''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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''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  :Union[str, Any]    						=	{f"""{i}""": i for i in range(SCREAMING_SNAKE_CASE			)}
        __UpperCamelCase  :Dict    						=	map_nested(lambda SCREAMING_SNAKE_CASE			: x + 10						,    SCREAMING_SNAKE_CASE						,    num_proc=SCREAMING_SNAKE_CASE						,    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_  (     UpperCAmelCase_				):






    '''simple docstring'''






    @require_tf
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        import tensorflow as tf
        from tensorflow.keras import layers

        __UpperCamelCase  :Optional[Any]    						=	layers.Dense(2)

        def gen_random_output():
            __UpperCamelCase  :Optional[Any]    						=	tf.random.uniform((1, 3))
            return model(__lowercase).numpy()

        with temp_seed(42				,				set_tensorflow=__lowercase):
            __UpperCamelCase  :Tuple    						=	gen_random_output()
        with temp_seed(42				,				set_tensorflow=__lowercase):
            __UpperCamelCase  :int    						=	gen_random_output()
        __UpperCamelCase  :List[str]    						=	gen_random_output()

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







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

        def gen_random_output():
            __UpperCamelCase  :List[Any]    						=	torch.nn.Linear(3				,				2)
            __UpperCamelCase  :Optional[Any]    						=	torch.rand(1				,				3)
            return model(__lowercase).detach().numpy()

        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :Union[str, Any]    						=	gen_random_output()
        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :List[str]    						=	gen_random_output()
        __UpperCamelCase  :Dict    						=	gen_random_output()

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







    def     UpperCamelCase__     (			self) ->			str:
        def gen_random_output():
            return np.random.rand(1				,				3)

        with temp_seed(42):
            __UpperCamelCase  :List[Any]    						=	gen_random_output()
        with temp_seed(42):
            __UpperCamelCase  :Optional[Any]    						=	gen_random_output()
        __UpperCamelCase  :Optional[Any]    						=	gen_random_output()

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



@pytest.mark.parametrize('''input_data'''						,    [{}]			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).flatten()
    assert output == expected_output



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	A(x=1						,    y='''foobar'''			)
    __UpperCamelCase  :Dict    						=	{'''x''': 1, '''y''': '''foobar'''}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

    __UpperCamelCase  :str    						=	{'''a''': {'''b''': A(x=10						,    y='''foo'''			)}, '''c''': [A(x=20						,    y='''bar'''			)]}
    __UpperCamelCase  :List[Any]    						=	{'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return text.split()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







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

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

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





