



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Dict									=		KandinskyInpaintPipeline
						UpperCAmelCase							:   int									=		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
						UpperCAmelCase							:   Any									=		[
						    "prompt",
						    "negative_prompt",
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						    "mask_image",
						]
						UpperCAmelCase							:   List[str]									=		[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "negative_prompt",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase							:   Optional[Any]									=		False


						@property
						def     __snake_case   ( self				)     ->     List[str]:
							return 32


						@property
						def     __snake_case   ( self				)     ->     Optional[Any]:
							return 32


						@property
						def     __snake_case   ( self				)     ->     int:
							return self.time_input_dim


						@property
						def     __snake_case   ( self				)     ->     Optional[Any]:
							return self.time_input_dim * 4


						@property
						def     __snake_case   ( self				)     ->     Optional[Any]:
							return 100


						@property
						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
							return tokenizer


						@property
						def     __snake_case   ( self				)     ->     Tuple:
							torch.manual_seed(0				)
							lowerCAmelCase   =     MCLIPConfig(
							    numDims=self.cross_attention_dim     ,	transformerDimensions=self.text_embedder_hidden_size     ,	hidden_size=self.text_embedder_hidden_size     ,	intermediate_size=37     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	vocab_size=1005     ,	)

							lowerCAmelCase   =     MultilingualCLIP(A_				)
							lowerCAmelCase   =     text_encoder.eval()

							return text_encoder


						@property
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							torch.manual_seed(0				)

							lowerCAmelCase   =     {
							    """in_channels""": 9,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							lowerCAmelCase   =     UNetaDConditionModel(**A_				)
							return model


						@property
						def     __snake_case   ( self				)     ->     int:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}


						@property
						def     __snake_case   ( self				)     ->     str:
							torch.manual_seed(0				)
							lowerCAmelCase   =     VQModel(**self.dummy_movq_kwargs				)
							return model


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.dummy_text_encoder
							lowerCAmelCase   =     self.dummy_tokenizer
							lowerCAmelCase   =     self.dummy_unet
							lowerCAmelCase   =     self.dummy_movq

							lowerCAmelCase   =     DDIMScheduler(
							    num_train_timesteps=1000     ,	beta_schedule="""linear"""     ,	beta_start=0.0_0_0_8_5     ,	beta_end=0.0_1_2     ,	clip_sample=A_     ,	set_alpha_to_one=A_     ,	steps_offset=1     ,	prediction_type="""epsilon"""     ,	thresholding=A_     ,	)

							lowerCAmelCase   =     {
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components


						def     __snake_case   ( self     ,	A_     ,	A_=0				)     ->     List[str]:
							lowerCAmelCase   =     floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(A_				)				).to(A_				)
							lowerCAmelCase   =     floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1				)				).to(A_				)
							# create init_image
							lowerCAmelCase   =     floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(A_				)				).to(A_				)
							lowerCAmelCase   =     image.cpu().permute(0     ,	2     ,	3     ,	1				)[0]
							lowerCAmelCase   =     Image.fromarray(np.uinta(A_				)				).convert("""RGB"""				).resize((256, 256)				)
							# create mask
							lowerCAmelCase   =     np.ones((64, 64)     ,	dtype=np.floataa				)
							lowerCAmelCase   =     0

							if str(A_				).startswith("""mps"""				):
								lowerCAmelCase   =     torch.manual_seed(A_				)
							else:
								lowerCAmelCase   =     torch.Generator(device=A_				).manual_seed(A_				)
							lowerCAmelCase   =     {
							    """prompt""": """horse""",
							    """image""": init_image,
							    """mask_image""": mask,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 4.0,
							    """output_type""": """np""",
							}
							return inputs


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     """cpu"""

							lowerCAmelCase   =     self.get_dummy_components()

							lowerCAmelCase   =     self.pipeline_class(**A_				)
							lowerCAmelCase   =     pipe.to(A_				)

							pipe.set_progress_bar_config(disable=A_				)

							lowerCAmelCase   =     pipe(**self.get_dummy_inputs(A_				)				)
							lowerCAmelCase   =     output.images

							lowerCAmelCase   =     pipe(
							    **self.get_dummy_inputs(A_				)     ,	return_dict=A_     ,	)[0]

							lowerCAmelCase   =     image[0, -3:, -3:, -1]
							lowerCAmelCase   =     image_from_tuple[0, -3:, -3:, -1]

							print(f'image.shape {image.shape}'				)

							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase   =     np.array(
							    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]				)

							assert (
							    np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
							), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
							), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""				)

							lowerCAmelCase   =     load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""				)
							lowerCAmelCase   =     np.ones((768, 768)     ,	dtype=np.floataa				)
							lowerCAmelCase   =     0

							lowerCAmelCase   =     """a hat"""

							lowerCAmelCase   =     KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,	torch_dtype=torch.floataa				)
							pipe_prior.to(A_				)

							lowerCAmelCase   =     KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""     ,	torch_dtype=torch.floataa				)
							lowerCAmelCase   =     pipeline.to(A_				)
							pipeline.set_progress_bar_config(disable=A_				)

							lowerCAmelCase   =     torch.Generator(device="""cpu"""				).manual_seed(0				)
							lowerCAmelCase,       lowerCAmelCase   =     pipe_prior(
							    A_     ,	generator=A_     ,	num_inference_steps=5     ,	negative_prompt=""""""     ,	).to_tuple()

							lowerCAmelCase   =     pipeline(
							    A_     ,	image=A_     ,	mask_image=A_     ,	image_embeds=A_     ,	negative_image_embeds=A_     ,	generator=A_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	output_type="""np"""     ,	)

							lowerCAmelCase   =     output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(A_     ,	A_				)




'''simple docstring'''

import pytest

import datasets


# Import fixture modules as plugins
UpperCAmelCase     		=   ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						Optional[int]:







	"""simple docstring"""
	# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
	for item in items:
		if any(marker in item.keywords for marker in ["""integration""", """unit"""]							):
			continue
		item.add_marker(pytest.mark.unit							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						Any:







	"""simple docstring"""
	config.addinivalue_line("""markers"""							,		"""torchaudio_latest: mark test to run with torchaudio>=0.12"""							)






@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE							)
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							)      ->						str:







	"""simple docstring"""
	# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
	lowerCAmelCase   =     tmp_path_factory.getbasetemp() / """cache"""
	lowerCAmelCase   =     test_hf_cache_home / """datasets"""
	lowerCAmelCase   =     test_hf_cache_home / """metrics"""
	lowerCAmelCase   =     test_hf_cache_home / """modules"""
	monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase   =     test_hf_datasets_cache / """downloads"""
	monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase   =     test_hf_datasets_cache / """downloads""" / """extracted"""
	monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""							,		str(_SCREAMING_SNAKE_CASE							)							)






@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE							,		scope="""session"""							)
def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	datasets.disable_progress_bar()






@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE							)
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							)      ->						Optional[int]:







	"""simple docstring"""
	# don't take tests into account when counting downloads
	monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""							,		_SCREAMING_SNAKE_CASE							)






@pytest.fixture
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						str:







	"""simple docstring"""
	# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
	# To be removed once SQLAlchemy 2.0 supported
	monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""							,		_SCREAMING_SNAKE_CASE							)