

'''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 		_A     (    A__      ):  # picklable for multiprocessing






 """simple docstring"""



 return x.sum()


def 		_A     (    A__      ):  # picklable for multiprocessing






 """simple docstring"""



 return i + 1






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  int
  SCREAMING_SNAKE_CASE    :  str






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = {}
   __lowercase							  = []
   __lowercase							  = 1
   __lowercase							  = [1, 2]
   __lowercase							  = {'''a''': 1, '''b''': 2}
   __lowercase							  = {'''a''': [1, 2], '''b''': [3, 4]}
   __lowercase							  = {'''a''': {'''1''': 1}, '''b''': 2}
   __lowercase							  = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
   __lowercase							  = {}
   __lowercase							  = []
   __lowercase							  = 2
   __lowercase							  = [2, 3]
   __lowercase							  = {'''a''': 2, '''b''': 3}
   __lowercase							  = {'''a''': [2, 3], '''b''': [4, 5]}
   __lowercase							  = {'''a''': {'''1''': 2}, '''b''': 3}
   __lowercase							  = {'''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__			)

   __lowercase							  = 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__			)

   __lowercase							  = {'''a''': np.eye(2			), '''b''': np.zeros(3			), '''c''': np.ones(2			)}
   __lowercase							  = {'''a''': 2, '''b''': 0, '''c''': 2}
   __lowercase							  = {
       '''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 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = {'''a''': 1, '''b''': 2}
   __lowercase							  = {'''a''': 3, '''b''': 4}
   __lowercase							  = {'''a''': 5, '''b''': 6}
   __lowercase							  = sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]			)
   self.assertEqual(sorted(zip_dict(lowercase__					,lowercase__					,lowercase__			)			)					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):






   class 			lowercase_	:




     """simple docstring"""




     SCREAMING_SNAKE_CASE    :  Optional[int]							=			'bar'
   __lowercase							  = 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 		_A     (    A__     ,				A__     ,				A__      ):






 """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:
  __lowercase							  = {F"{i}": i for i in range(A__      )}
  __lowercase							  = 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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @require_tf
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   import tensorflow as tf
   from tensorflow.keras import layers

   __lowercase							  = layers.Dense(2			)

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

   with temp_seed(4_2					,set_tensorflow=lowercase__			):
    __lowercase							  = gen_random_output()
   with temp_seed(4_2					,set_tensorflow=lowercase__			):
    __lowercase							  = gen_random_output()
   __lowercase							  = gen_random_output()

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






  @require_torch
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   import torch

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

   with temp_seed(4_2					,set_pytorch=lowercase__			):
    __lowercase							  = gen_random_output()
   with temp_seed(4_2					,set_pytorch=lowercase__			):
    __lowercase							  = gen_random_output()
   __lowercase							  = gen_random_output()

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






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

   with temp_seed(4_2			):
    __lowercase							  = gen_random_output()
   with temp_seed(4_2			):
    __lowercase							  = gen_random_output()
   __lowercase							  = gen_random_output()

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


@pytest.mark.parametrize('''input_data'''     ,				[{}]      )
def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = 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 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = NestedDataStructure(A__      ).flatten()
 assert output == expected_output


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = A(x=1     ,				y='''foobar'''      )
 __lowercase							  = {'''x''': 1, '''y''': '''foobar'''}
 assert asdict(A__      ) == expected_output

 __lowercase							  = {'''a''': {'''b''': A(x=10     ,				y='''foo'''      )}, '''c''': [A(x=20     ,				y='''bar'''      )]}
 __lowercase							  = {'''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 		_A     (    A__      ):






 """simple docstring"""



 return text.split()


def 		_A     (    A__      ):






 """simple docstring"""



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






def 		_A     (    ):






 """simple docstring"""



 with Pool(2      ) as pool:
  __lowercase							  = 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:
  __lowercase							  = 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:
  __lowercase							  = []
  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




'''simple docstring'''






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)

