


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 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Dict  				=			KandinskyInpaintPipeline
							_lowercase     :      List[str]  				=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							_lowercase     :      Tuple  				=			[
							    '''prompt''',
							    '''negative_prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							_lowercase     :      Optional[int]  				=			[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''negative_prompt''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							_lowercase     :      str  				=			False

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Optional[int]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       Union[str, Any]:





									"""simple docstring"""







									return self.time_input_dim

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       List[Any]:





									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									return 100

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Tuple:





									"""simple docstring"""







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

							@property
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Dict:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				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   ,						)

									lowercase__						=				MultilingualCLIP(UpperCamelCase_							)
									lowercase__						=				text_encoder.eval()

									return text_encoder

							@property
							def 				lowerCamelCase_			(							self:     Any							)      ->       List[Any]:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''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,
									}

									lowercase__						=				UNetaDConditionModel(**UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""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 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				VQModel(**self.dummy_movq_kwargs							)
									return model

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.dummy_text_encoder
									lowercase__						=				self.dummy_tokenizer
									lowercase__						=				self.dummy_unet
									lowercase__						=				self.dummy_movq

									lowercase__						=				DDIMScheduler(
									    num_train_timesteps=1_000   ,						beta_schedule='''linear'''   ,						beta_start=0.00085   ,						beta_end=0.012   ,						clip_sample=UpperCamelCase_   ,						set_alpha_to_one=UpperCamelCase_   ,						steps_offset=1   ,						prediction_type='''epsilon'''   ,						thresholding=UpperCamelCase_   ,						)

									lowercase__						=				{
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Union[str, Any]=0							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
									lowercase__						=				floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(seed + 1							)							).to(UpperCamelCase_							)
									# create init_image
									lowercase__						=				floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
									lowercase__						=				image.cpu().permute(0   ,						2   ,						3   ,						1							)[0]
									lowercase__						=				Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((256, 256)							)
									# create mask
									lowercase__						=				np.ones((64, 64)   ,						dtype=np.floataa							)
									lowercase__						=				0

									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__						=				torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__						=				{
									    '''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 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				'''cpu'''

									lowercase__						=				self.get_dummy_components()

									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)

									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				pipe(**self.get_dummy_inputs(UpperCamelCase_							)							)
									lowercase__						=				output.images

									lowercase__						=				pipe(
									    **self.get_dummy_inputs(UpperCamelCase_							)   ,						return_dict=UpperCamelCase_   ,						)[0]

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

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

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

									lowercase__						=				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 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







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


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

							def 				lowerCamelCase_			(							self:     Dict							)      ->       int:





									"""simple docstring"""







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


							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''							)

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

									lowercase__						=				'''a hat'''

									lowercase__						=				KandinskyPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-prior'''   ,						torch_dtype=torch.floataa							)
									pipe_prior.to(UpperCamelCase_							)

									lowercase__						=				KandinskyInpaintPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-inpaint'''   ,						torch_dtype=torch.floataa							)
									lowercase__						=				pipeline.to(UpperCamelCase_							)
									pipeline.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				torch.Generator(device='''cpu'''							).manual_seed(0							)
									lowercase__					,		lowercase__						=				pipe_prior(
									    UpperCamelCase_   ,						generator=UpperCamelCase_   ,						num_inference_steps=5   ,						negative_prompt=''''''   ,						).to_tuple()

									lowercase__						=				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'''   ,						)

									lowercase__						=				output.images[0]

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

									assert_mean_pixel_difference(UpperCamelCase_   ,						UpperCamelCase_							)






def       _a			(				SCREAMING_SNAKE_CASE = 10_00					):


		"""simple docstring"""




		return sum(e for e in range(3						,    SCREAMING_SNAKE_CASE					) if e % 3 == 0 or e % 5 == 0					)


if __name__ == "__main__":
			print(f"""{solution() = }""")


