
"""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 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[int]       =     KandinskyInpaintPipeline
				__UpperCamelCase   :		int       =     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
				__UpperCamelCase   :		Optional[int]       =     [
				    '''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   :		List[Any]       =     False



				@property
				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											return 32



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



											"""simple docstring"""




											return 32



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return self.time_input_dim



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



											"""simple docstring"""




											return self.time_input_dim * 4



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return 1_00



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
											return tokenizer



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



											"""simple docstring"""




											torch.manual_seed(0					)
											SCREAMING_SNAKE_CASE__		: List[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     , )

											SCREAMING_SNAKE_CASE__		: str     =     MultilingualCLIP(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     text_encoder.eval()

											return text_encoder



				@property
				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											torch.manual_seed(0					)

											SCREAMING_SNAKE_CASE__		: Dict     =     {
											    """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,
											}

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     UNetaDConditionModel(**SCREAMING_SNAKE_CASE__					)
											return model



				@property
				def 			__magic_name__					(self					)      ->	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 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     DDIMScheduler(
											    num_train_timesteps=10_00     , beta_schedule="""linear"""     , beta_start=0.00085     , beta_end=0.012     , clip_sample=SCREAMING_SNAKE_CASE__     , set_alpha_to_one=SCREAMING_SNAKE_CASE__     , steps_offset=1     , prediction_type="""epsilon"""     , thresholding=SCREAMING_SNAKE_CASE__     , )

											SCREAMING_SNAKE_CASE__		: int     =     {
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}

											return components



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=0					)      ->	Union[str, Any]:



											"""simple docstring"""




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

											if str(SCREAMING_SNAKE_CASE__					).startswith("""mps"""					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     torch.manual_seed(SCREAMING_SNAKE_CASE__					)
											else:
																		SCREAMING_SNAKE_CASE__		: Any     =     torch.Generator(device=SCREAMING_SNAKE_CASE__					).manual_seed(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {
											    """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 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     """cpu"""

											SCREAMING_SNAKE_CASE__		: int     =     self.get_dummy_components()

											SCREAMING_SNAKE_CASE__		: Dict     =     self.pipeline_class(**SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     pipe.to(SCREAMING_SNAKE_CASE__					)

											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: int     =     pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__					)					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output.images

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

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

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

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

											SCREAMING_SNAKE_CASE__		: str     =     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 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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






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


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

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

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """a hat"""

											SCREAMING_SNAKE_CASE__		: List[Any]     =     KandinskyPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-1-prior"""     , torch_dtype=torch.floataa					)
											pipe_prior.to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     KandinskyInpaintPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-1-inpaint"""     , torch_dtype=torch.floataa					)
											SCREAMING_SNAKE_CASE__		: int     =     pipeline.to(SCREAMING_SNAKE_CASE__					)
											pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     torch.Generator(device="""cpu"""					).manual_seed(0					)
											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Any     =     pipe_prior(
											    SCREAMING_SNAKE_CASE__     , generator=SCREAMING_SNAKE_CASE__     , num_inference_steps=5     , negative_prompt=""""""     , ).to_tuple()

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     pipeline(
											    SCREAMING_SNAKE_CASE__     , image=SCREAMING_SNAKE_CASE__     , mask_image=SCREAMING_SNAKE_CASE__     , image_embeds=SCREAMING_SNAKE_CASE__     , negative_image_embeds=SCREAMING_SNAKE_CASE__     , generator=SCREAMING_SNAKE_CASE__     , num_inference_steps=1_00     , height=7_68     , width=7_68     , output_type="""np"""     , )

											SCREAMING_SNAKE_CASE__		: Dict     =     output.images[0]

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

											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




def lowercase_						(     _snake_case     ,_snake_case		):
							return int((input_a, input_a).count(1		) != 0		)

def lowercase_						(     ):
							assert or_gate(0     ,0		) == 0
							assert or_gate(0     ,1		) == 1
							assert or_gate(1     ,0		) == 1
							assert or_gate(1     ,1		) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))




