


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        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						List[str]       )			->					Dict:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
														__snake_case	:    Any											=     1
														__snake_case	:    Optional[int]											=     3
														__snake_case	:    Tuple											=     (32, 32)

														__snake_case	:    Tuple											=     floats_tensor((batch_size, num_channels) + sizes					,			rng=random.Random(0       )       ).to(lowerCamelCase       )
														return image

							@property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[Any]:
														torch.manual_seed(0       )
														__snake_case	:    str											=     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 					__snake_case      (							self :						int       )			->					Optional[int]:
														torch.manual_seed(0       )
														__snake_case	:    Dict											=     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 					__snake_case      (							self :						Dict       )			->					Tuple:
														torch.manual_seed(0       )
														__snake_case	:    Optional[Any]											=     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=5006					,			)
														return RobertaSeriesModelWithTransformation(lowerCamelCase       )

							@property
							def 					__snake_case      (							self :						Any       )			->					Union[str, Any]:
														def extract(*lowerCamelCase :						int					,			**lowerCamelCase :						Union[str, Any]       ):

																					class        a	:




																												"""simple docstring"""


																												def __init__(							self :						List[Any]       )			->					int:
																																			__snake_case	:    Optional[Any]											=     torch.ones([0]       )






																												def 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						List[str]       )			->					Union[str, Any]:
																																			self.pixel_values.to(lowerCamelCase       )
																																			return self
																					return Out()

														return extract

							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    List[Any]											=     "cpu"  # ensure determinism for the device-dependent torch.Generator
														__snake_case	:    Optional[int]											=     self.dummy_cond_unet
														__snake_case	:    int											=     PNDMScheduler(skip_prk_steps=lowerCamelCase       )
														__snake_case	:    Optional[int]											=     self.dummy_vae
														__snake_case	:    Any											=     self.dummy_text_encoder
														__snake_case	:    Optional[Any]											=     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
														__snake_case	:    List[str]											=     77

														__snake_case	:    Optional[Any]											=     self.dummy_image.to(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     init_image / 2 + 0.5

														# make sure here that pndm scheduler skips prk
														__snake_case	:    Any											=     AltDiffusionImgaImgPipeline(
														    unet=lowerCamelCase					,			scheduler=lowerCamelCase					,			vae=lowerCamelCase					,			text_encoder=lowerCamelCase					,			tokenizer=lowerCamelCase					,			safety_checker=lowerCamelCase					,			feature_extractor=self.dummy_extractor					,			)
														__snake_case	:    Tuple											=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,			do_normalize=lowerCamelCase       )
														__snake_case	:    Dict											=     alt_pipe.to(lowerCamelCase       )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    List[str]											=     "A painting of a squirrel eating a burger"
														__snake_case	:    List[Any]											=     torch.Generator(device=lowerCamelCase       ).manual_seed(0       )
														__snake_case	:    Any											=     alt_pipe(
														    [prompt]					,			generator=lowerCamelCase					,			guidance_scale=6.0					,			num_inference_steps=2					,			output_type="np"					,			image=lowerCamelCase					,			)

														__snake_case	:    Optional[int]											=     output.images

														__snake_case	:    Optional[Any]											=     torch.Generator(device=lowerCamelCase       ).manual_seed(0       )
														__snake_case	:    List[str]											=     alt_pipe(
														    [prompt]					,			generator=lowerCamelCase					,			guidance_scale=6.0					,			num_inference_steps=2					,			output_type="np"					,			image=lowerCamelCase					,			return_dict=lowerCamelCase					,			)[0]

														__snake_case	:    Union[str, Any]											=     image[0, -3:, -3:, -1]
														__snake_case	:    Optional[int]											=     image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														__snake_case	:    Optional[int]											=     np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99]       )

														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 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.dummy_cond_unet
														__snake_case	:    List[Any]											=     PNDMScheduler(skip_prk_steps=lowerCamelCase       )
														__snake_case	:    int											=     self.dummy_vae
														__snake_case	:    str											=     self.dummy_text_encoder
														__snake_case	:    Optional[Any]											=     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
														__snake_case	:    Optional[int]											=     77

														__snake_case	:    Any											=     self.dummy_image.to(lowerCamelCase       )

														# put models in fp16
														__snake_case	:    Any											=     unet.half()
														__snake_case	:    List[Any]											=     vae.half()
														__snake_case	:    List[str]											=     bert.half()

														# make sure here that pndm scheduler skips prk
														__snake_case	:    int											=     AltDiffusionImgaImgPipeline(
														    unet=lowerCamelCase					,			scheduler=lowerCamelCase					,			vae=lowerCamelCase					,			text_encoder=lowerCamelCase					,			tokenizer=lowerCamelCase					,			safety_checker=lowerCamelCase					,			feature_extractor=self.dummy_extractor					,			)
														__snake_case	:    List[str]											=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,			do_normalize=lowerCamelCase       )
														__snake_case	:    List[str]											=     alt_pipe.to(lowerCamelCase       )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Dict											=     "A painting of a squirrel eating a burger"
														__snake_case	:    str											=     torch.manual_seed(0       )
														__snake_case	:    List[Any]											=     alt_pipe(
														    [prompt]					,			generator=lowerCamelCase					,			num_inference_steps=2					,			output_type="np"					,			image=lowerCamelCase					,			).images

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






							@unittest.skipIf(torch_device != "cuda"					,			"This test requires a GPU"       )
							def 					__snake_case      (							self :						List[str]       )			->					List[Any]:
														__snake_case	:    Dict											=     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
														__snake_case	:    Tuple											=     init_image.resize((760, 504)       )

														__snake_case	:    str											=     "BAAI/AltDiffusion"
														__snake_case	:    List[Any]											=     AltDiffusionImgaImgPipeline.from_pretrained(
														    lowerCamelCase					,			safety_checker=lowerCamelCase					,			)
														pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )
														pipe.enable_attention_slicing()

														__snake_case	:    Union[str, Any]											=     "A fantasy landscape, trending on artstation"

														__snake_case	:    List[str]											=     torch.manual_seed(0       )
														__snake_case	:    Tuple											=     pipe(
														    prompt=lowerCamelCase					,			image=lowerCamelCase					,			strength=0.75					,			guidance_scale=7.5					,			generator=lowerCamelCase					,			output_type="np"					,			)
														__snake_case	:    int											=     output.images[0]

														__snake_case	:    Tuple											=     image[255:258, 383:386, -1]

														assert image.shape == (504, 760, 3)
														__snake_case	:    List[str]											=     np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00]       )

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

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




							"""simple docstring"""


							def 					__snake_case      (							self :						List[str]       )			->					Dict:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					__snake_case      (							self :						Optional[int]       )			->					List[Any]:
														__snake_case	:    List[str]											=     load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg"       )
														__snake_case	:    int											=     init_image.resize((768, 512)       )
														__snake_case	:    Optional[Any]											=     load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"       )

														__snake_case	:    int											=     "BAAI/AltDiffusion"
														__snake_case	:    Any											=     AltDiffusionImgaImgPipeline.from_pretrained(
														    lowerCamelCase					,			safety_checker=lowerCamelCase					,			)
														pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )
														pipe.enable_attention_slicing()

														__snake_case	:    Optional[Any]											=     "A fantasy landscape, trending on artstation"

														__snake_case	:    Dict											=     torch.manual_seed(0       )
														__snake_case	:    List[Any]											=     pipe(
														    prompt=lowerCamelCase					,			image=lowerCamelCase					,			strength=0.75					,			guidance_scale=7.5					,			generator=lowerCamelCase					,			output_type="np"					,			)
														__snake_case	:    Optional[Any]											=     output.images[0]

														assert image.shape == (512, 768, 3)
														# img2img is flaky across GPUs even in fp32, so using MAE here
														assert np.abs(expected_image - image       ).max() < 1E-2





from maths.prime_check import is_prime







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if not isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    List[str]											=     F'Input value of [number={number}] must be an integer'
														raise TypeError(__lowerCamelCase			)
							if is_prime(__lowerCamelCase			) and is_prime(number + 2			):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

