


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




							@property
							def   __a   (     self:    Tuple   ):
										return 32




							@property
							def   __a   (     self:    Union[str, Any]   ):
										return 32




							@property
							def   __a   (     self:    Tuple   ):
										return self.time_input_dim




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




							@property
							def   __a   (     self:    str   ):
										return 1_00




							@property
							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""   )
										return tokenizer




							@property
							def   __a   (     self:    Union[str, Any]   ):
										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=10_05     ,							)

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

										return text_encoder




							@property
							def   __a   (     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   __a   (     self:    Optional[Any]   ):
										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   __a   (     self:    Dict   ):
										torch.manual_seed(0   )
										__lowerCamelCase          =      VQModel(**self.dummy_movq_kwargs   )
										return model




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.dummy_text_encoder
										__lowerCamelCase          =      self.dummy_tokenizer
										__lowerCamelCase          =      self.dummy_unet
										__lowerCamelCase          =      self.dummy_movq

										__lowerCamelCase          =      DDIMScheduler(
										    num_train_timesteps=10_00     ,							beta_schedule="""linear"""     ,							beta_start=0.00_085     ,							beta_end=0.012     ,							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   __a   (     self:    Union[str, Any]     ,							A_:    Optional[Any]     ,							A_:    str=0   ):
										__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((2_56, 2_56)   )
										# 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   __a   (     self:    List[str]   ):
										__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_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]   )

										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   __a   (     self:    Union[str, Any]   ):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )




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




							def   __a   (     self:    Any   ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




							def   __a   (     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((7_68, 7_68)     ,							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=1_00     ,							height=7_68     ,							width=7_68     ,							output_type="""np"""     ,							)

										__lowerCamelCase          =      output.images[0]

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

										assert_mean_pixel_difference(A_     ,							A_   )








"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




