





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'''







							lowerCAmelCase__						=					KandinskyInpaintPipeline
							lowerCAmelCase__						=					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
							lowerCAmelCase__						=					[
							    'prompt',
							    'negative_prompt',
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							    'mask_image',
							]
							lowerCAmelCase__						=					[
							    'generator',
							    'height',
							    'width',
							    'latents',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowerCAmelCase__						=					False




							@property
							def 							UpperCAmelCase__			(  self			:   Dict		):
									return 32




							@property
							def 							UpperCAmelCase__			(  self			:   Optional[int]		):
									return 32




							@property
							def 							UpperCAmelCase__			(  self			:   str		):
									return self.time_input_dim




							@property
							def 							UpperCAmelCase__			(  self			:   List[str]		):
									return self.time_input_dim * 4




							@property
							def 							UpperCAmelCase__			(  self			:   List[str]		):
									return 100




							@property
							def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
									__snake_case:						List[str]			  =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
									return tokenizer




							@property
							def 							UpperCAmelCase__			(  self			:   List[Any]		):
									torch.manual_seed(0		)
									__snake_case:						Optional[Any]			  =						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=1_005							,    )

									__snake_case:						Optional[Any]			  =						MultilingualCLIP(UpperCamelCase_		)
									__snake_case:						Tuple			  =						text_encoder.eval()

									return text_encoder




							@property
							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									torch.manual_seed(0		)

									__snake_case:						Tuple			  =						{
									    """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,
									}

									__snake_case:						Any			  =						UNetaDConditionModel(**UpperCamelCase_		)
									return model




							@property
							def 							UpperCAmelCase__			(  self			:   Dict		):
									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 							UpperCAmelCase__			(  self			:   List[Any]		):
									torch.manual_seed(0		)
									__snake_case:						Tuple			  =						VQModel(**self.dummy_movq_kwargs		)
									return model




							def 							UpperCAmelCase__			(  self			:   Dict		):
									__snake_case:						Union[str, Any]			  =						self.dummy_text_encoder
									__snake_case:						List[str]			  =						self.dummy_tokenizer
									__snake_case:						str			  =						self.dummy_unet
									__snake_case:						List[Any]			  =						self.dummy_movq

									__snake_case:						int			  =						DDIMScheduler(
									    num_train_timesteps=1_000							,    beta_schedule="""linear"""							,    beta_start=0.0_0085							,    beta_end=0.012							,    clip_sample=UpperCamelCase_							,    set_alpha_to_one=UpperCamelCase_							,    steps_offset=1							,    prediction_type="""epsilon"""							,    thresholding=UpperCamelCase_							,    )

									__snake_case:						Union[str, Any]			  =						{
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}

									return components




							def 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   Optional[Any]							,    A			:   Optional[Any]=0		):
									__snake_case:						List[str]			  =						floats_tensor((1, self.cross_attention_dim)							,    rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
									__snake_case:						Tuple			  =						floats_tensor((1, self.cross_attention_dim)							,    rng=random.Random(seed + 1		)		).to(UpperCamelCase_		)
									# create init_image
									__snake_case:						Union[str, Any]			  =						floats_tensor((1, 3, 64, 64)							,    rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
									__snake_case:						Tuple			  =						image.cpu().permute(0							,    2							,    3							,    1		)[0]
									__snake_case:						Optional[int]			  =						Image.fromarray(np.uinta(UpperCamelCase_		)		).convert("""RGB"""		).resize((256, 256)		)
									# create mask
									__snake_case:						int			  =						np.ones((64, 64)							,    dtype=np.floataa		)
									__snake_case:						Tuple			  =						0

									if str(UpperCamelCase_		).startswith("""mps"""		):
											__snake_case:						List[str]			  =						torch.manual_seed(UpperCamelCase_		)
									else:
											__snake_case:						List[str]			  =						torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
									__snake_case:						int			  =						{
									    """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 							UpperCAmelCase__			(  self			:   List[Any]		):
									__snake_case:						Tuple			  =						"""cpu"""

									__snake_case:						int			  =						self.get_dummy_components()

									__snake_case:						str			  =						self.pipeline_class(**UpperCamelCase_		)
									__snake_case:						Dict			  =						pipe.to(UpperCamelCase_		)

									pipe.set_progress_bar_config(disable=UpperCamelCase_		)

									__snake_case:						List[str]			  =						pipe(**self.get_dummy_inputs(UpperCamelCase_		)		)
									__snake_case:						List[Any]			  =						output.images

									__snake_case:						str			  =						pipe(
									    **self.get_dummy_inputs(UpperCamelCase_		)							,    return_dict=UpperCamelCase_							,    )[0]

									__snake_case:						Tuple			  =						image[0, -3:, -3:, -1]
									__snake_case:						Dict			  =						image_from_tuple[0, -3:, -3:, -1]

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

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

									__snake_case:						Optional[int]			  =						np.array(
									    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]		)

									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 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







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





							'''simple docstring'''







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






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

									__snake_case:						List[str]			  =						load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
									__snake_case:						Union[str, Any]			  =						np.ones((768, 768)							,    dtype=np.floataa		)
									__snake_case:						Union[str, Any]			  =						0

									__snake_case:						Tuple			  =						"""a hat"""

									__snake_case:						Optional[int]			  =						KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""							,    torch_dtype=torch.floataa		)
									pipe_prior.to(UpperCamelCase_		)

									__snake_case:						str			  =						KandinskyInpaintPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-inpaint"""							,    torch_dtype=torch.floataa		)
									__snake_case:						Any			  =						pipeline.to(UpperCamelCase_		)
									pipeline.set_progress_bar_config(disable=UpperCamelCase_		)

									__snake_case:						Union[str, Any]			  =						torch.Generator(device="""cpu"""		).manual_seed(0		)
									__snake_case  ,       __snake_case:						List[Any]			  =						pipe_prior(
									    UpperCamelCase_							,    generator=UpperCamelCase_							,    num_inference_steps=5							,    negative_prompt=""""""							,    ).to_tuple()

									__snake_case:						Tuple			  =						pipeline(
									    UpperCamelCase_							,    image=UpperCamelCase_							,    mask_image=UpperCamelCase_							,    image_embeds=UpperCamelCase_							,    negative_image_embeds=UpperCamelCase_							,    generator=UpperCamelCase_							,    num_inference_steps=100							,    height=768							,    width=768							,    output_type="""np"""							,    )

									__snake_case:						Union[str, Any]			  =						output.images[0]

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

									assert_mean_pixel_difference(UpperCamelCase_							,    UpperCamelCase_		)













import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)






