





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 			lowercase				(							SCREAMING_SNAKE_CASE__		,			unittest.TestCase     ):



						"""simple docstring"""

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







						@property
						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										return 32







						@property
						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										return 32







						@property
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										return self.time_input_dim







						@property
						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										return self.time_input_dim * 4







						@property
						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										return 1_00







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
										return tokenizer







						@property
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			Union[str, 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=10_05 ,				)

										_snake_case :			List[Any]  						=   MultilingualCLIP(_lowercase					)
										_snake_case :			List[str]  						=   text_encoder.eval()

										return text_encoder







						@property
						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										torch.manual_seed(0					)

										_snake_case :			Any  						=   {
										    """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 :			List[str]  						=   UNetaDConditionModel(**_lowercase					)
										return model







						@property
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										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			:		Optional[int]					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			List[str]  						=   VQModel(**self.dummy_movq_kwargs					)
										return model







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			int  						=   self.dummy_text_encoder
										_snake_case :			Tuple  						=   self.dummy_tokenizer
										_snake_case :			Union[str, Any]  						=   self.dummy_unet
										_snake_case :			Dict  						=   self.dummy_movq

										_snake_case :			Any  						=   DDIMScheduler(
										    num_train_timesteps=10_00 ,				beta_schedule='linear' ,				beta_start=0.00085 ,				beta_end=0.012 ,				clip_sample=_lowercase ,				set_alpha_to_one=_lowercase ,				steps_offset=1 ,				prediction_type='epsilon' ,				thresholding=_lowercase ,				)

										_snake_case :			List[Any]  						=   {
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		List[str]=0					):





										'''simple docstring'''







										_snake_case :			int  						=   floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(_lowercase					)					).to(_lowercase					)
										_snake_case :			List[Any]  						=   floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(seed + 1					)					).to(_lowercase					)
										# create init_image
										_snake_case :			Optional[int]  						=   floats_tensor((1, 3, 64, 64) ,				rng=random.Random(_lowercase					)					).to(_lowercase					)
										_snake_case :			str  						=   image.cpu().permute(0 ,				2 ,				3 ,				1					)[0]
										_snake_case :			Any  						=   Image.fromarray(np.uinta(_lowercase					)					).convert('RGB'					).resize((2_56, 2_56)					)
										# create mask
										_snake_case :			Optional[Any]  						=   np.ones((64, 64) ,				dtype=np.floataa					)
										_snake_case :			Tuple  						=   0

										if str(_lowercase					).startswith('mps'					):
														_snake_case :			List[str]  						=   torch.manual_seed(_lowercase					)
										else:
														_snake_case :			int  						=   torch.Generator(device=_lowercase					).manual_seed(_lowercase					)
										_snake_case :			Tuple  						=   {
										    """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			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			int  						=   """cpu"""

										_snake_case :			Optional[int]  						=   self.get_dummy_components()

										_snake_case :			Dict  						=   self.pipeline_class(**_lowercase					)
										_snake_case :			Optional[int]  						=   pipe.to(_lowercase					)

										pipe.set_progress_bar_config(disable=_lowercase					)

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

										_snake_case :			Dict  						=   pipe(
										    **self.get_dummy_inputs(_lowercase					) ,				return_dict=_lowercase ,				)[0]

										_snake_case :			Any  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[str]  						=   image_from_tuple[0, -3:, -3:, -1]

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

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

										_snake_case :			Union[str, Any]  						=   np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]					)

										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			:		str					):





										'''simple docstring'''







										super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



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



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_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 :			Tuple  						=   load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
										_snake_case :			str  						=   np.ones((7_68, 7_68) ,				dtype=np.floataa					)
										_snake_case :			Union[str, Any]  						=   0

										_snake_case :			Any  						=   """a hat"""

										_snake_case :			str  						=   KandinskyPriorPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-1-prior' ,				torch_dtype=torch.floataa					)
										pipe_prior.to(_lowercase					)

										_snake_case :			Union[str, Any]  						=   KandinskyInpaintPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-1-inpaint' ,				torch_dtype=torch.floataa					)
										_snake_case :			List[str]  						=   pipeline.to(_lowercase					)
										pipeline.set_progress_bar_config(disable=_lowercase					)

										_snake_case :			Any  						=   torch.Generator(device='cpu'					).manual_seed(0					)
										_snake_case :			Optional[Any]  						=   pipe_prior(
										    _lowercase ,				generator=_lowercase ,				num_inference_steps=5 ,				negative_prompt='' ,				).to_tuple()

										_snake_case :			str  						=   pipeline(
										    _lowercase ,				image=_lowercase ,				mask_image=_lowercase ,				image_embeds=_lowercase ,				negative_image_embeds=_lowercase ,				generator=_lowercase ,				num_inference_steps=1_00 ,				height=7_68 ,				width=7_68 ,				output_type='np' ,				)

										_snake_case :			Any  						=   output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(_lowercase ,				_lowercase					)













def       A__(		__lowerCAmelCase ):

				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise TypeError('only integers accepted as input' )
				else:
								_snake_case :			Any  						=   str(abs(__lowerCAmelCase ) )
								_snake_case :			List[str]  						=   [list(__lowerCAmelCase ) for char in range(len(__lowerCAmelCase ) )]
								for index in range(len(__lowerCAmelCase ) ):
												num_transpositions[index].pop(__lowerCAmelCase )
								return max(
								    int(''.join(list(__lowerCAmelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
		__import__('''doctest''').testmod()






