





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    _a     (				lowercase__					,							lowercase__					,							lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						AltDiffusionPipeline
							snake_case_							 =						TEXT_TO_IMAGE_PARAMS
							snake_case_							 =						TEXT_TO_IMAGE_BATCH_PARAMS
							snake_case_							 =						TEXT_TO_IMAGE_IMAGE_PARAMS
							snake_case_							 =						TEXT_TO_IMAGE_IMAGE_PARAMS

							def      A_  (      self				:     Optional[int]    )	->Dict:
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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							,	)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					DDIMScheduler(
													    beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule="scaled_linear"							,	clip_sample=a							,	set_alpha_to_one=a							,	)
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					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    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					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=50_02							,	)
													SCREAMING_SNAKE_CASE__     :				Tuple   =					CLIPTextModel(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					77

													SCREAMING_SNAKE_CASE__     :				str   =					{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components

							def      A_  (      self				:     Tuple							,	a				:     List[str]							,	a				:     int=0    )	->Union[str, Any]:
													if str(a    ).startswith("mps"    ):
																			SCREAMING_SNAKE_CASE__     :				str   =					torch.manual_seed(a    )
													else:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.Generator(device=a    ).manual_seed(a    )
													SCREAMING_SNAKE_CASE__     :				int   =					{
													    "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      A_  (      self				:     int    )	->List[Any]:
													super().test_attention_slicing_forward_pass(expected_max_diff=3E-3    )

							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													super().test_inference_batch_single_identical(expected_max_diff=3E-3    )

							def      A_  (      self				:     List[Any]    )	->int:
													SCREAMING_SNAKE_CASE__     :				Dict   =					"cpu"  # ensure determinism for the device-dependent torch.Generator

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.get_dummy_components()
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				str   =					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=50_02							,	)
													# TODO: remove after fixing the non-deterministic text encoder
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					RobertaSeriesModelWithTransformation(a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					text_encoder

													SCREAMING_SNAKE_CASE__     :				str   =					AltDiffusionPipeline(**a    )
													SCREAMING_SNAKE_CASE__     :				str   =					alt_pipe.to(a    )
													alt_pipe.set_progress_bar_config(disable=a    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.get_dummy_inputs(a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					"A photo of an astronaut"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					alt_pipe(**a    )
													SCREAMING_SNAKE_CASE__     :				str   =					output.images
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					np.array(
													    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]    )

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






							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"cpu"  # ensure determinism for the device-dependent torch.Generator

													SCREAMING_SNAKE_CASE__     :				Dict   =					self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :				Tuple   =					PNDMScheduler(skip_prk_steps=a    )
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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=50_02							,	)
													# TODO: remove after fixing the non-deterministic text encoder
													SCREAMING_SNAKE_CASE__     :				Any   =					RobertaSeriesModelWithTransformation(a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					text_encoder
													SCREAMING_SNAKE_CASE__     :				List[str]   =					AltDiffusionPipeline(**a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					alt_pipe.to(a    )
													alt_pipe.set_progress_bar_config(disable=a    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.get_dummy_inputs(a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					alt_pipe(**a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					output.images
													SCREAMING_SNAKE_CASE__     :				int   =					image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					np.array(
													    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]    )

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







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






							"""simple docstring"""

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

							def      A_  (      self				:     Optional[int]    )	->Union[str, Any]:
													# make sure here that pndm scheduler skips prk
													SCREAMING_SNAKE_CASE__     :				Any   =					AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"							,	safety_checker=a    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					alt_pipe.to(a    )
													alt_pipe.set_progress_bar_config(disable=a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"A painting of a squirrel eating a burger"
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					alt_pipe([prompt]							,	generator=a							,	guidance_scale=6.0							,	num_inference_steps=20							,	output_type="np"    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					output.images

													SCREAMING_SNAKE_CASE__     :				Any   =					image[0, -3:, -3:, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :				List[str]   =					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]    )

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






							def      A_  (      self				:     List[str]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					DDIMScheduler.from_pretrained("BAAI/AltDiffusion"							,	subfolder="scheduler"    )

													SCREAMING_SNAKE_CASE__     :				str   =					AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"							,	scheduler=a							,	safety_checker=a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					alt_pipe.to(a    )
													alt_pipe.set_progress_bar_config(disable=a    )

													SCREAMING_SNAKE_CASE__     :				int   =					"A painting of a squirrel eating a burger"
													SCREAMING_SNAKE_CASE__     :				Dict   =					torch.manual_seed(0    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					alt_pipe([prompt]							,	generator=a							,	num_inference_steps=2							,	output_type="numpy"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					output.images

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image[0, -3:, -3:, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :				Dict   =					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]    )

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










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 1_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					-1
						SCREAMING_SNAKE_CASE__     :				str   =					0
						for a in range(1			,   n // 3     ):
												# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
												SCREAMING_SNAKE_CASE__     :				Tuple   =					(n * n - 2 * a * n) // (2 * n - 2 * a)
												SCREAMING_SNAKE_CASE__     :				Dict   =					n - a - b
												if c * c == (a * a + b * b):
																		SCREAMING_SNAKE_CASE__     :				str   =					a * b * c
																		if candidate >= product:
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					candidate
						return product


if __name__ == "__main__":
				print(f"{solution() = }")