






"""simple docstring"""

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						A_							(      snake_case_					,							snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				StableDiffusionSAGPipeline
				UpperCAmelCase__        =				TEXT_TO_IMAGE_PARAMS
				UpperCAmelCase__        =				TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCAmelCase__        =				TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCAmelCase__        =				TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										torch.manual_seed(0	)
										__magic_name__            =     UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)						,    layers_per_block=2						,    sample_size=3_2						,    in_channels=4						,    out_channels=4						,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,    cross_attention_dim=3_2						,    )
										__magic_name__            =     DDIMScheduler(
										    beta_start=0.0_0085						,    beta_end=0.012						,    beta_schedule="scaled_linear"						,    clip_sample=__lowerCamelCase						,    set_alpha_to_one=__lowerCamelCase						,    )
										torch.manual_seed(0	)
										__magic_name__            =     AutoencoderKL(
										    block_out_channels=[3_2, 6_4]						,    in_channels=3						,    out_channels=3						,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,    latent_channels=4						,    )
										torch.manual_seed(0	)
										__magic_name__            =     CLIPTextConfig(
										    bos_token_id=0						,    eos_token_id=2						,    hidden_size=3_2						,    intermediate_size=3_7						,    layer_norm_eps=1e-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1_0_0_0						,    )
										__magic_name__            =     CLIPTextModel(__lowerCamelCase	)
										__magic_name__            =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Dict=0	)				->					Any:
										if str(__lowerCamelCase	).startswith("mps"	):
																__magic_name__            =     torch.manual_seed(__lowerCamelCase	)
										else:
																__magic_name__            =     torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
										__magic_name__            =     {
										    "prompt": ".",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 1.0,
										    "sag_scale": 1.0,
										    "output_type": "numpy",
										}
										return inputs







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[Any]:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3	)







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







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







				def     _snake_case					(							self							:						Dict	)				->					Any:
										__magic_name__            =     StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	)
										__magic_name__            =     sag_pipe.to(__lowerCamelCase	)
										sag_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     "."
										__magic_name__            =     torch.manual_seed(0	)
										__magic_name__            =     sag_pipe(
										    [prompt]						,    generator=__lowerCamelCase						,    guidance_scale=7.5						,    sag_scale=1.0						,    num_inference_steps=2_0						,    output_type="np"	)

										__magic_name__            =     output.images

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

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										__magic_name__            =     np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-2







				def     _snake_case					(							self							:						Dict	)				->					str:
										__magic_name__            =     StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"	)
										__magic_name__            =     sag_pipe.to(__lowerCamelCase	)
										sag_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     "."
										__magic_name__            =     torch.manual_seed(0	)
										__magic_name__            =     sag_pipe(
										    [prompt]						,    generator=__lowerCamelCase						,    guidance_scale=7.5						,    sag_scale=1.0						,    num_inference_steps=2_0						,    output_type="np"	)

										__magic_name__            =     output.images

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

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										__magic_name__            =     np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-2







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										__magic_name__            =     StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"	)
										__magic_name__            =     sag_pipe.to(__lowerCamelCase	)
										sag_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     "."
										__magic_name__            =     torch.manual_seed(0	)
										__magic_name__            =     sag_pipe(
										    [prompt]						,    width=7_6_8						,    height=5_1_2						,    generator=__lowerCamelCase						,    guidance_scale=7.5						,    sag_scale=1.0						,    num_inference_steps=2_0						,    output_type="np"						,    )

										__magic_name__            =     output.images

										assert image.shape == (1, 5_1_2, 7_6_8, 3)











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



