





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_						(				_UpperCamelCase )		->						Optional[Any]:  # picklable for multiprocessing






				"""simple docstring"""


				return x.sum()






def      lowerCamelCase_						(				_UpperCamelCase )		->						str:  # picklable for multiprocessing






				"""simple docstring"""


				return i + 1



@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     int
			lowerCamelCase_     :     str




class 					__lowerCAmelCase       (			_a     ):






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  {}
							snake_case_						:    Tuple				=  []
							snake_case_						:    Optional[Any]				=  1
							snake_case_						:    str				=  [1, 2]
							snake_case_						:    str				=  {'''a''': 1, '''b''': 2}
							snake_case_						:    int				=  {'''a''': [1, 2], '''b''': [3, 4]}
							snake_case_						:    List[Any]				=  {'''a''': {'''1''': 1}, '''b''': 2}
							snake_case_						:    Optional[int]				=  {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
							snake_case_						:    str				=  {}
							snake_case_						:    int				=  []
							snake_case_						:    Any				=  2
							snake_case_						:    Optional[Any]				=  [2, 3]
							snake_case_						:    Tuple				=  {'''a''': 2, '''b''': 3}
							snake_case_						:    Any				=  {'''a''': [2, 3], '''b''': [4, 5]}
							snake_case_						:    Union[str, Any]				=  {'''a''': {'''1''': 2}, '''b''': 3}
							snake_case_						:    Dict				=  {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__    )					,							__magic_name__    )

							snake_case_						:    Tuple				=  2
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )
							self.assertEqual(map_nested(__magic_name__					,							__magic_name__					,							num_proc=__magic_name__    )					,							__magic_name__    )

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






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    str				=  {'''a''': 1, '''b''': 2}
							snake_case_						:    Optional[int]				=  {'''a''': 3, '''b''': 4}
							snake_case_						:    Dict				=  {'''a''': 5, '''b''': 6}
							snake_case_						:    str				=  sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]    )
							self.assertEqual(sorted(zip_dict(__magic_name__					,							__magic_name__					,							__magic_name__    )    )					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							class 					__lowerCAmelCase       :
										lowerCamelCase_     :     Dict       			=      '''bar'''




							snake_case_						:    List[Any]				=  Foo()
							self.assertEqual(foo.my_attr					,							'''bar'''    )
							with temporary_assignment(__magic_name__					,							'''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_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Union[str, Any]:






				"""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:
								snake_case_						:    str				=  {f'''{i}''': i for i in range(_UpperCamelCase )}
								snake_case_						:    Tuple				=  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 					__lowerCAmelCase       (			_a     ):






			@require_tf
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							import tensorflow as tf
							from tensorflow.keras import layers

							snake_case_						:    str				=  layers.Dense(2    )

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

							with temp_seed(42					,							set_tensorflow=__magic_name__    ):
											snake_case_						:    int				=  gen_random_output()
							with temp_seed(42					,							set_tensorflow=__magic_name__    ):
											snake_case_						:    Optional[int]				=  gen_random_output()
							snake_case_						:    Any				=  gen_random_output()

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






			@require_torch
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							import torch

							def gen_random_output():
											snake_case_						:    Dict				=  torch.nn.Linear(3					,							2    )
											snake_case_						:    List[Any]				=  torch.rand(1					,							3    )
											return model(__magic_name__    ).detach().numpy()

							with temp_seed(42					,							set_pytorch=__magic_name__    ):
											snake_case_						:    Optional[Any]				=  gen_random_output()
							with temp_seed(42					,							set_pytorch=__magic_name__    ):
											snake_case_						:    str				=  gen_random_output()
							snake_case_						:    str				=  gen_random_output()

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






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


							'''simple docstring'''



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

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

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






@pytest.mark.parametrize('''input_data''' ,				[{}] )
def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_						:    Any				=  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      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Optional[int]				=  NestedDataStructure(_UpperCamelCase ).flatten()
				assert output == expected_output






def      lowerCamelCase_						(				)		->						int:






				"""simple docstring"""


				snake_case_						:    Dict				=  A(x=1 ,				y='''foobar''' )
				snake_case_						:    List[Any]				=  {'''x''': 1, '''y''': '''foobar'''}
				assert asdict(_UpperCamelCase ) == expected_output

				snake_case_						:    Optional[Any]				=  {'''a''': {'''b''': A(x=10 ,				y='''foo''' )}, '''c''': [A(x=20 ,				y='''bar''' )]}
				snake_case_						:    Optional[int]				=  {'''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      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				return text.split()






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						int:






				"""simple docstring"""


				with Pool(2 ) as pool:
								snake_case_						:    str				=  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:
								snake_case_						:    Optional[int]				=  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:
								snake_case_						:    Tuple				=  []
								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







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
