


"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()




class 			__snake_case   (unittest.TestCase  ):




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




							@property
							def   __a   (     self:    str   ):
										__lowerCamelCase          =      1
										__lowerCamelCase          =      3
										__lowerCamelCase          =      (32, 32)

										__lowerCamelCase          =      floats_tensor((batch_size, num_channels) + sizes     ,							rng=random.Random(0   )   ).to(A_   )
										return image




							@property
							def   __a   (     self:    Optional[int]   ):
										torch.manual_seed(0   )
										__lowerCamelCase          =      UNetaDConditionModel(
										    block_out_channels=(32, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,							cross_attention_dim=32     ,							)
										return model




							@property
							def   __a   (     self:    int   ):
										torch.manual_seed(0   )
										__lowerCamelCase          =      AutoencoderKL(
										    block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,							latent_channels=4     ,							)
										return model




							@property
							def   __a   (     self:    Tuple   ):
										torch.manual_seed(0   )
										__lowerCamelCase          =      RobertaSeriesConfig(
										    hidden_size=32     ,							project_dim=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=50_06     ,							)
										return RobertaSeriesModelWithTransformation(A_   )




							@property
							def   __a   (     self:    List[str]   ):
										def extract(*A_:    Dict     ,							**A_:    Optional[int]   ):




													class 			__snake_case   :




																				def __init__(     self:    Optional[Any]   ):
																							__lowerCamelCase          =      torch.ones([0]   )




																				def   __a   (     self:    Optional[Any]     ,							A_:    Optional[Any]   ):
																							self.pixel_values.to(A_   )
																							return self

													return Out()

										return extract




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
										__lowerCamelCase          =      self.dummy_cond_unet
										__lowerCamelCase          =      PNDMScheduler(skip_prk_steps=A_   )
										__lowerCamelCase          =      self.dummy_vae
										__lowerCamelCase          =      self.dummy_text_encoder
										__lowerCamelCase          =      XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
										__lowerCamelCase          =      77

										__lowerCamelCase          =      self.dummy_image.to(A_   )
										__lowerCamelCase          =      init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										__lowerCamelCase          =      AltDiffusionImgaImgPipeline(
										    unet=A_     ,							scheduler=A_     ,							vae=A_     ,							text_encoder=A_     ,							tokenizer=A_     ,							safety_checker=A_     ,							feature_extractor=self.dummy_extractor     ,							)
										__lowerCamelCase          =      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor     ,							do_normalize=A_   )
										__lowerCamelCase          =      alt_pipe.to(A_   )
										alt_pipe.set_progress_bar_config(disable=A_   )

										__lowerCamelCase          =      """A painting of a squirrel eating a burger"""
										__lowerCamelCase          =      torch.Generator(device=A_   ).manual_seed(0   )
										__lowerCamelCase          =      alt_pipe(
										    [prompt]     ,							generator=A_     ,							guidance_scale=6.0     ,							num_inference_steps=2     ,							output_type="""np"""     ,							image=A_     ,							)

										__lowerCamelCase          =      output.images

										__lowerCamelCase          =      torch.Generator(device=A_   ).manual_seed(0   )
										__lowerCamelCase          =      alt_pipe(
										    [prompt]     ,							generator=A_     ,							guidance_scale=6.0     ,							num_inference_steps=2     ,							output_type="""np"""     ,							image=A_     ,							return_dict=A_     ,							)[0]

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

										assert image.shape == (1, 32, 32, 3)
										__lowerCamelCase          =      np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5E-3




							@unittest.skipIf(torch_device != """cuda"""     ,							"""This test requires a GPU"""   )
							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.dummy_cond_unet
										__lowerCamelCase          =      PNDMScheduler(skip_prk_steps=A_   )
										__lowerCamelCase          =      self.dummy_vae
										__lowerCamelCase          =      self.dummy_text_encoder
										__lowerCamelCase          =      XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
										__lowerCamelCase          =      77

										__lowerCamelCase          =      self.dummy_image.to(A_   )

										# put models in fp16
										__lowerCamelCase          =      unet.half()
										__lowerCamelCase          =      vae.half()
										__lowerCamelCase          =      bert.half()

										# make sure here that pndm scheduler skips prk
										__lowerCamelCase          =      AltDiffusionImgaImgPipeline(
										    unet=A_     ,							scheduler=A_     ,							vae=A_     ,							text_encoder=A_     ,							tokenizer=A_     ,							safety_checker=A_     ,							feature_extractor=self.dummy_extractor     ,							)
										__lowerCamelCase          =      VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor     ,							do_normalize=A_   )
										__lowerCamelCase          =      alt_pipe.to(A_   )
										alt_pipe.set_progress_bar_config(disable=A_   )

										__lowerCamelCase          =      """A painting of a squirrel eating a burger"""
										__lowerCamelCase          =      torch.manual_seed(0   )
										__lowerCamelCase          =      alt_pipe(
										    [prompt]     ,							generator=A_     ,							num_inference_steps=2     ,							output_type="""np"""     ,							image=A_     ,							).images

										assert image.shape == (1, 32, 32, 3)




							@unittest.skipIf(torch_device != """cuda"""     ,							"""This test requires a GPU"""   )
							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/img2img/sketch-mountains-input.jpg"""   )
										# resize to resolution that is divisible by 8 but not 16 or 32
										__lowerCamelCase          =      init_image.resize((7_60, 5_04)   )

										__lowerCamelCase          =      """BAAI/AltDiffusion"""
										__lowerCamelCase          =      AltDiffusionImgaImgPipeline.from_pretrained(
										    A_     ,							safety_checker=A_     ,							)
										pipe.to(A_   )
										pipe.set_progress_bar_config(disable=A_   )
										pipe.enable_attention_slicing()

										__lowerCamelCase          =      """A fantasy landscape, trending on artstation"""

										__lowerCamelCase          =      torch.manual_seed(0   )
										__lowerCamelCase          =      pipe(
										    prompt=A_     ,							image=A_     ,							strength=0.75     ,							guidance_scale=7.5     ,							generator=A_     ,							output_type="""np"""     ,							)
										__lowerCamelCase          =      output.images[0]

										__lowerCamelCase          =      image[2_55:2_58, 3_83:3_86, -1]

										assert image.shape == (5_04, 7_60, 3)
										__lowerCamelCase          =      np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




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




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




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/img2img/sketch-mountains-input.jpg"""   )
										__lowerCamelCase          =      init_image.resize((7_68, 5_12)   )
										__lowerCamelCase          =      load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""   )

										__lowerCamelCase          =      """BAAI/AltDiffusion"""
										__lowerCamelCase          =      AltDiffusionImgaImgPipeline.from_pretrained(
										    A_     ,							safety_checker=A_     ,							)
										pipe.to(A_   )
										pipe.set_progress_bar_config(disable=A_   )
										pipe.enable_attention_slicing()

										__lowerCamelCase          =      """A fantasy landscape, trending on artstation"""

										__lowerCamelCase          =      torch.manual_seed(0   )
										__lowerCamelCase          =      pipe(
										    prompt=A_     ,							image=A_     ,							strength=0.75     ,							guidance_scale=7.5     ,							generator=A_     ,							output_type="""np"""     ,							)
										__lowerCamelCase          =      output.images[0]

										assert image.shape == (5_12, 7_68, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image   ).max() < 1E-2








"""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.""")




