



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Optional[Any]								=     AltDiffusionPipeline
				A_   :     Union[str, Any]								=     TEXT_TO_IMAGE_PARAMS
				A_   :     int								=     TEXT_TO_IMAGE_BATCH_PARAMS
				A_   :     Optional[Any]								=     TEXT_TO_IMAGE_IMAGE_PARAMS
				A_   :     Optional[Any]								=     TEXT_TO_IMAGE_IMAGE_PARAMS


				def __UpperCamelCase						(							self      :  Dict       )		->     Union[str, Any]:
											torch.manual_seed(0       )
											A      						=    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						, )
											A      						=    DDIMScheduler(
											    beta_start=0.0_0_0_8_5						, beta_end=0.0_1_2						, beta_schedule='scaled_linear'						, clip_sample=__UpperCamelCase						, set_alpha_to_one=__UpperCamelCase						, )
											torch.manual_seed(0       )
											A      						=    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						, )

											# TODO: address the non-deterministic text encoder (fails for save-load tests)
											# torch.manual_seed(0)
											# text_encoder_config = RobertaSeriesConfig(
											#     hidden_size=32,
											#     project_dim=32,
											#     intermediate_size=37,
											#     layer_norm_eps=1e-05,
											#     num_attention_heads=4,
											#     num_hidden_layers=5,
											#     vocab_size=5002,
											# )
											# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

											torch.manual_seed(0       )
											A      						=    CLIPTextConfig(
											    bos_token_id=0						, eos_token_id=2						, hidden_size=32						, projection_dim=32						, intermediate_size=37						, layer_norm_eps=1e-05						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=5_002						, )
											A      						=    CLIPTextModel(__UpperCamelCase       )

											A      						=    XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
											A      						=    77

											A      						=    {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Dict=0       )		->     int:
											if str(__UpperCamelCase       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(__UpperCamelCase       )
											else:
																		A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
											A      						=    {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[Any]:
											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     str:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator

											A      						=    self.get_dummy_components()
											torch.manual_seed(0       )
											A      						=    RobertaSeriesConfig(
											    hidden_size=32						, project_dim=32						, intermediate_size=37						, layer_norm_eps=1e-05						, num_attention_heads=4						, num_hidden_layers=5						, vocab_size=5_002						, )
											# TODO: remove after fixing the non-deterministic text encoder
											A      						=    RobertaSeriesModelWithTransformation(__UpperCamelCase       )
											A      						=    text_encoder

											A      						=    AltDiffusionPipeline(**__UpperCamelCase       )
											A      						=    alt_pipe.to(__UpperCamelCase       )
											alt_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    'A photo of an astronaut'
											A      						=    alt_pipe(**__UpperCamelCase       )
											A      						=    output.images
											A      						=    image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											A      						=    np.array(
											    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]       )

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


				def __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											A      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator

											A      						=    self.get_dummy_components()
											A      						=    PNDMScheduler(skip_prk_steps=__UpperCamelCase       )
											torch.manual_seed(0       )
											A      						=    RobertaSeriesConfig(
											    hidden_size=32						, project_dim=32						, intermediate_size=37						, layer_norm_eps=1e-05						, num_attention_heads=4						, num_hidden_layers=5						, vocab_size=5_002						, )
											# TODO: remove after fixing the non-deterministic text encoder
											A      						=    RobertaSeriesModelWithTransformation(__UpperCamelCase       )
											A      						=    text_encoder
											A      						=    AltDiffusionPipeline(**__UpperCamelCase       )
											A      						=    alt_pipe.to(__UpperCamelCase       )
											alt_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    alt_pipe(**__UpperCamelCase       )
											A      						=    output.images
											A      						=    image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											A      						=    np.array(
											    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]       )

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





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


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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											# make sure here that pndm scheduler skips prk
											A      						=    AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						, safety_checker=__UpperCamelCase       )
											A      						=    alt_pipe.to(__UpperCamelCase       )
											alt_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    'A painting of a squirrel eating a burger'
											A      						=    torch.manual_seed(0       )
											A      						=    alt_pipe([prompt]						, generator=__UpperCamelCase						, guidance_scale=6.0						, num_inference_steps=20						, output_type='np'       )

											A      						=    output.images

											A      						=    image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]       )

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


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											A      						=    DDIMScheduler.from_pretrained('BAAI/AltDiffusion'						, subfolder='scheduler'       )

											A      						=    AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						, scheduler=__UpperCamelCase						, safety_checker=__UpperCamelCase       )
											A      						=    alt_pipe.to(__UpperCamelCase       )
											alt_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    'A painting of a squirrel eating a burger'
											A      						=    torch.manual_seed(0       )

											A      						=    alt_pipe([prompt]						, generator=__UpperCamelCase						, num_inference_steps=2						, output_type='numpy'       )
											A      						=    output.images

											A      						=    image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).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.""")




