
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__							=				logging.get_logger(__name__)


lowerCAmelCase__							=				['model.decoder.embed_positions.weights']



def        _lowerCamelCase ( __a    ):
		if "emb" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''emb''',							'''model.decoder.embed_tokens'''    )
		if "transformer" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''transformer''',							'''model.decoder'''    )
		if "cross_attention" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''cross_attention''',							'''encoder_attn'''    )
		if "linear1" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''linear1''',							'''fc1'''    )
		if "linear2" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''linear2''',							'''fc2'''    )
		if "norm1" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''norm1''',							'''self_attn_layer_norm'''    )
		if "norm_cross" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''norm_cross''',							'''encoder_attn_layer_norm'''    )
		if "norm2" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''norm2''',							'''final_layer_norm'''    )
		if "out_norm" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''out_norm''',							'''model.decoder.layer_norm'''    )
		if "linears" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''linears''',							'''lm_heads'''    )
		if "condition_provider.conditioners.description.output_proj" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''condition_provider.conditioners.description.output_proj''',							'''enc_to_dec_proj'''    )
		return name



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = list(state_dict.keys()    )
		SCREAMING_SNAKE_CASE_						 = {}
		for key in keys:
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(_SCREAMING_SNAKE_CASE    )
				SCREAMING_SNAKE_CASE_						 = rename_keys(_SCREAMING_SNAKE_CASE    )
				if "in_proj_weight" in key:
						# split fused qkv proj
						SCREAMING_SNAKE_CASE_						 = val[:hidden_size, :]
						SCREAMING_SNAKE_CASE_						 = val[hidden_size : 2 * hidden_size, :]
						SCREAMING_SNAKE_CASE_						 = val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						SCREAMING_SNAKE_CASE_						 = val
				else:
						SCREAMING_SNAKE_CASE_						 = val
		return state_dict, enc_dec_proj_state_dict



def        _lowerCamelCase ( __a    ):
		if checkpoint == "small":
				# default config values
				SCREAMING_SNAKE_CASE_						 = 1_024
				SCREAMING_SNAKE_CASE_						 = 24
				SCREAMING_SNAKE_CASE_						 = 16
		elif checkpoint == "medium":
				SCREAMING_SNAKE_CASE_						 = 1_536
				SCREAMING_SNAKE_CASE_						 = 48
				SCREAMING_SNAKE_CASE_						 = 24
		elif checkpoint == "large":
				SCREAMING_SNAKE_CASE_						 = 2_048
				SCREAMING_SNAKE_CASE_						 = 48
				SCREAMING_SNAKE_CASE_						 = 32
		else:
				raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'    )
		SCREAMING_SNAKE_CASE_						 = MusicgenDecoderConfig(
		    hidden_size=_SCREAMING_SNAKE_CASE,							ffn_dim=hidden_size * 4,							num_hidden_layers=_SCREAMING_SNAKE_CASE,							num_attention_heads=_SCREAMING_SNAKE_CASE,							)
		return config



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a=None,							__a=None,							__a="cpu"    ):
		SCREAMING_SNAKE_CASE_						 = MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE,							device=_SCREAMING_SNAKE_CASE    )
		SCREAMING_SNAKE_CASE_						 = decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE    )

		SCREAMING_SNAKE_CASE_						 = fairseq_model.lm.state_dict()
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = rename_state_dict(
		    _SCREAMING_SNAKE_CASE,							hidden_size=decoder_config.hidden_size    )

		SCREAMING_SNAKE_CASE_						 = TaEncoderModel.from_pretrained('''t5-base'''    )
		SCREAMING_SNAKE_CASE_						 = EncodecModel.from_pretrained('''facebook/encodec_32khz'''    )
		SCREAMING_SNAKE_CASE_						 = MusicgenForCausalLM(_SCREAMING_SNAKE_CASE    ).eval()

		# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = decoder.load_state_dict(_SCREAMING_SNAKE_CASE,							strict=_SCREAMING_SNAKE_CASE    )

		for key in missing_keys.copy():
				if key.startswith(('''text_encoder''', '''audio_encoder''')    ) or key in EXPECTED_MISSING_KEYS:
						missing_keys.remove(_SCREAMING_SNAKE_CASE    )

		if len(_SCREAMING_SNAKE_CASE    ) > 0:
				raise ValueError(F'Missing key(s) in state_dict: {missing_keys}'    )

		if len(_SCREAMING_SNAKE_CASE    ) > 0:
				raise ValueError(F'Unexpected key(s) in state_dict: {unexpected_keys}'    )

		# init the composite model
		SCREAMING_SNAKE_CASE_						 = MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE,							audio_encoder=_SCREAMING_SNAKE_CASE,							decoder=_SCREAMING_SNAKE_CASE    )

		# load the pre-trained enc-dec projection (from the decoder state dict)
		model.enc_to_dec_proj.load_state_dict(_SCREAMING_SNAKE_CASE    )

		# check we can do a forward pass
		SCREAMING_SNAKE_CASE_						 = torch.arange(0,							8,							dtype=torch.long    ).reshape(2,							-1    )
		SCREAMING_SNAKE_CASE_						 = input_ids.reshape(2 * 4,							-1    )

		with torch.no_grad():
				SCREAMING_SNAKE_CASE_						 = model(input_ids=_SCREAMING_SNAKE_CASE,							decoder_input_ids=_SCREAMING_SNAKE_CASE    ).logits

		if logits.shape != (8, 1, 2_048):
				raise ValueError('''Incorrect shape for logits'''    )

		# now construct the processor
		SCREAMING_SNAKE_CASE_						 = AutoTokenizer.from_pretrained('''t5-base'''    )
		SCREAMING_SNAKE_CASE_						 = AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''',							padding_side='''left'''    )

		SCREAMING_SNAKE_CASE_						 = MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE,							tokenizer=_SCREAMING_SNAKE_CASE    )

		# set the appropriate bos/pad token ids
		SCREAMING_SNAKE_CASE_						 = 2_048
		SCREAMING_SNAKE_CASE_						 = 2_048

		# set other default generation config params
		SCREAMING_SNAKE_CASE_						 = int(30 * audio_encoder.config.frame_rate    )
		SCREAMING_SNAKE_CASE_						 = True
		SCREAMING_SNAKE_CASE_						 = 3.0

		if pytorch_dump_folder is not None:
				Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
				logger.info(F'Saving model {checkpoint} to {pytorch_dump_folder}'    )
				model.save_pretrained(_SCREAMING_SNAKE_CASE    )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

		if repo_id:
				logger.info(F'Pushing model {checkpoint} to {repo_id}'    )
				model.push_to_hub(_SCREAMING_SNAKE_CASE    )
				processor.push_to_hub(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						lowerCAmelCase__							=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint',
						    default='small',
						    type=str,
						    help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder',
						    required=True,
						    default=None,
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
						)
						parser.add_argument(
						    '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
						)

						lowerCAmelCase__							=				parser.parse_args()
						convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

"""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        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return x.sum()



def        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return i + 1





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42







class  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = [1, 2]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [1, 2], '''b''': [3, 4]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 1}, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = [2, 3]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [2, 3], '''b''': [4, 5]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 2}, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''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_    )

									SCREAMING_SNAKE_CASE_						 = 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_    )

									SCREAMING_SNAKE_CASE_						 = {'''a''': np.eye(2    ), '''b''': np.zeros(3    ), '''c''': np.ones(2    )}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 0, '''c''': 2}
									SCREAMING_SNAKE_CASE_						 = {
									    '''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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 3, '''b''': 4}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 5, '''b''': 6}
									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									class  snake_case     :
																UpperCAmelCase__						     =      '''bar'''







									SCREAMING_SNAKE_CASE_						 = 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        _lowerCamelCase ( __a,							__a,							__a    ):
		with patch('''datasets.utils.py_utils._single_map_nested'''    ) as mock_single_map_nested, patch(
		    '''datasets.parallel.parallel.Pool'''    ) as mock_multiprocessing_pool:
				SCREAMING_SNAKE_CASE_						 = {F'{i}': i for i in range(__a    )}
				SCREAMING_SNAKE_CASE_						 = 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  snake_case     (							__lowercase   ):

							@require_tf
							def _lowercase					(self    ):

									"""simple docstring"""







									import tensorflow as tf
									from tensorflow.keras import layers

									SCREAMING_SNAKE_CASE_						 = layers.Dense(2    )

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

									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

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

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

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

									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

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







							def _lowercase					(self    ):

									"""simple docstring"""







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

									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = 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        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = 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,							__a    ):
		SCREAMING_SNAKE_CASE_						 = NestedDataStructure(__a    ).flatten()
		assert output == expected_output



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = A(x=1,							y='''foobar'''    )
		SCREAMING_SNAKE_CASE_						 = {'''x''': 1, '''y''': '''foobar'''}
		assert asdict(__a    ) == expected_output

		SCREAMING_SNAKE_CASE_						 = {'''a''': {'''b''': A(x=10,							y='''foo'''    )}, '''c''': [A(x=20,							y='''bar'''    )]}
		SCREAMING_SNAKE_CASE_						 = {'''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    ):
		return text.split()



def        _lowerCamelCase ( __a    ):
		yield (time.time(), content)
		time.sleep(2    )
		yield (time.time(), content)



def        _lowerCamelCase ( ):
		with Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = []
				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