

'''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 						UpperCamelCase	(			a			)				->     Dict:  # picklable for multiprocessing






     '''simple docstring'''
     return x.sum()




def 						UpperCamelCase	(			a			)				->     Optional[int]:  # picklable for multiprocessing






     '''simple docstring'''
     return i + 1






@dataclass
class   _SCREAMING_SNAKE_CASE   :
 __SCREAMING_SNAKE_CASE					:int
 __SCREAMING_SNAKE_CASE					:str






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    {}
      __magic_name__						       =    []
      __magic_name__						       =    1
      __magic_name__						       =    [1, 2]
      __magic_name__						       =    {'''a''': 1, '''b''': 2}
      __magic_name__						       =    {'''a''': [1, 2], '''b''': [3, 4]}
      __magic_name__						       =    {'''a''': {'''1''': 1}, '''b''': 2}
      __magic_name__						       =    {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
      __magic_name__						       =    {}
      __magic_name__						       =    []
      __magic_name__						       =    2
      __magic_name__						       =    [2, 3]
      __magic_name__						       =    {'''a''': 2, '''b''': 3}
      __magic_name__						       =    {'''a''': [2, 3], '''b''': [4, 5]}
      __magic_name__						       =    {'''a''': {'''1''': 2}, '''b''': 3}
      __magic_name__						       =    {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__      )			,				a__      )

      __magic_name__						       =    2
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )
      self.assertEqual(map_nested(a__			,				a__			,				num_proc=a__      )			,				a__      )

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



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    {'''a''': 1, '''b''': 2}
      __magic_name__						       =    {'''a''': 3, '''b''': 4}
      __magic_name__						       =    {'''a''': 5, '''b''': 6}
      __magic_name__						       =    sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]      )
      self.assertEqual(sorted(zip_dict(a__			,				a__			,				a__      )      )			,				a__      )



 def 			snake_case__  (   self		:   Tuple      ):






      class   _SCREAMING_SNAKE_CASE   :
       __SCREAMING_SNAKE_CASE					:int      	=					"""bar"""


      __magic_name__						       =    Foo()
      self.assertEqual(foo.my_attr			,				'''bar'''      )
      with temporary_assignment(a__			,				'''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 						UpperCamelCase	(			a		,						a		,						a			)				->     str:






     '''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:
          __magic_name__						       =    {F'''{i}''': i for i in range(a			)}
          __magic_name__						       =    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   _SCREAMING_SNAKE_CASE   (   __a     ):



 @require_tf
 def 			snake_case__  (   self		:   Optional[Any]      ):
      import tensorflow as tf
      from tensorflow.keras import layers

      __magic_name__						       =    layers.Dense(2      )

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

      with temp_seed(42			,				set_tensorflow=a__      ):
           __magic_name__						       =    gen_random_output()
      with temp_seed(42			,				set_tensorflow=a__      ):
           __magic_name__						       =    gen_random_output()
      __magic_name__						       =    gen_random_output()

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



 @require_torch
 def 			snake_case__  (   self		:   Optional[int]      ):
      import torch

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

      with temp_seed(42			,				set_pytorch=a__      ):
           __magic_name__						       =    gen_random_output()
      with temp_seed(42			,				set_pytorch=a__      ):
           __magic_name__						       =    gen_random_output()
      __magic_name__						       =    gen_random_output()

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



 def 			snake_case__  (   self		:   Optional[Any]      ):
      def gen_random_output():
           return np.random.rand(1			,				3      )

      with temp_seed(42      ):
           __magic_name__						       =    gen_random_output()
      with temp_seed(42      ):
           __magic_name__						       =    gen_random_output()
      __magic_name__						       =    gen_random_output()

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




@pytest.mark.parametrize('''input_data'''		,						[{}]			)
def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    NestedDataStructure(a			).flatten()
     assert output == expected_output




def 						UpperCamelCase	(			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__						       =    A(x=1		,						y='''foobar'''			)
     __magic_name__						       =    {'''x''': 1, '''y''': '''foobar'''}
     assert asdict(a			) == expected_output

     __magic_name__						       =    {'''a''': {'''b''': A(x=10		,						y='''foo'''			)}, '''c''': [A(x=20		,						y='''bar'''			)]}
     __magic_name__						       =    {'''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 						UpperCamelCase	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     return text.split()




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     yield (time.time(), content)
     time.sleep(2			)
     yield (time.time(), content)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     with Pool(2			) as pool:
          __magic_name__						       =    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:
          __magic_name__						       =    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:
          __magic_name__						       =    []
          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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					StableDiffusionInpaintPipeline
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE					:Tuple      	=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE					:str      	=					frozenset(
     []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					frozenset([]     )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    UNetaDConditionModel(
          block_out_channels=(32, 64)			,				layers_per_block=2			,				sample_size=32			,				in_channels=9			,				out_channels=4			,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,				cross_attention_dim=32			,				attention_head_dim=(2, 4)			,				use_linear_projection=a__			,				)
      __magic_name__						       =    PNDMScheduler(skip_prk_steps=a__      )
      torch.manual_seed(0      )
      __magic_name__						       =    AutoencoderKL(
          block_out_channels=[32, 64]			,				in_channels=3			,				out_channels=3			,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,				latent_channels=4			,				sample_size=128			,				)
      torch.manual_seed(0      )
      __magic_name__						       =    CLIPTextConfig(
          bos_token_id=0			,				eos_token_id=2			,				hidden_size=32			,				intermediate_size=37			,				layer_norm_eps=1E-05			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1000			,				hidden_act='''gelu'''			,				projection_dim=512			,				)
      __magic_name__						       =    CLIPTextModel(a__      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

      __magic_name__						       =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[int]			,				a__		:   List[Any]=0      ):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      __magic_name__						       =    floats_tensor((1, 3, 32, 32)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      )[0]
      __magic_name__						       =    Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )
      __magic_name__						       =    Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': init_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    StableDiffusionInpaintPipeline(**a__      )
      __magic_name__						       =    sd_pipe.to(a__      )
      sd_pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    self.get_dummy_inputs(a__      )
      __magic_name__						       =    sd_pipe(**a__      ).images
      __magic_name__						       =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __magic_name__						       =    np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]      )

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2



 def 			snake_case__  (   self		:   List[Any]      ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






@slow
@require_torch_gpu
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   int      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(a__			,				safety_checker=a__      )
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 9E-3



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				torch_dtype=torch.floataa			,				safety_checker=a__			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 5E-1



 def 			snake_case__  (   self		:   List[Any]      ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    PNDMScheduler.from_pretrained(a__			,				subfolder='''scheduler'''      )
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				safety_checker=a__			,				scheduler=a__			,				torch_dtype=torch.floataa			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing(1      )
      pipe.enable_sequential_cpu_offload()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				num_inference_steps=2			,				output_type='''np'''			,				)

      __magic_name__						       =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






