


'''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__				( __A							,						__A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Dict	=	StableDiffusionSAGPipeline
				__UpperCamelCase    :		Tuple	=	TEXT_TO_IMAGE_PARAMS
				__UpperCamelCase    :		int	=	TEXT_TO_IMAGE_BATCH_PARAMS
				__UpperCamelCase    :		Optional[int]	=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__UpperCamelCase    :		Optional[Any]	=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__UpperCamelCase    :		Optional[int]	=	False




				def 					_snake_case  (self		):
										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       ,			)
										__lowerCAmelCase							       =				DDIMScheduler(
										    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule='''scaled_linear'''       ,			clip_sample=__lowercase       ,			set_alpha_to_one=__lowercase       ,			)
										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       ,			)
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				CLIPTextConfig(
										    bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=10_00       ,			)
										__lowerCAmelCase							       =				CLIPTextModel(__lowercase		)
										__lowerCAmelCase							       =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=0		):
										if str(__lowercase		).startswith('''mps'''		):
																__lowerCAmelCase							       =				torch.manual_seed(__lowercase		)
										else:
																__lowerCAmelCase							       =				torch.Generator(device=__lowercase		).manual_seed(__lowercase		)
										__lowerCAmelCase							       =				{
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 					_snake_case  (self		):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3		)


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







				"""simple docstring"""



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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''		)
										__lowerCAmelCase							       =				sag_pipe.to(__lowercase		)
										sag_pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				'''.'''
										__lowerCAmelCase							       =				torch.manual_seed(0		)
										__lowerCAmelCase							       =				sag_pipe(
										    [prompt]       ,			generator=__lowercase       ,			guidance_scale=7.5       ,			sag_scale=1.0       ,			num_inference_steps=20       ,			output_type='''np'''		)

										__lowerCAmelCase							       =				output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =				np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''		)
										__lowerCAmelCase							       =				sag_pipe.to(__lowercase		)
										sag_pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				'''.'''
										__lowerCAmelCase							       =				torch.manual_seed(0		)
										__lowerCAmelCase							       =				sag_pipe(
										    [prompt]       ,			generator=__lowercase       ,			guidance_scale=7.5       ,			sag_scale=1.0       ,			num_inference_steps=20       ,			output_type='''np'''		)

										__lowerCAmelCase							       =				output.images

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

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =				np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]		)

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''		)
										__lowerCAmelCase							       =				sag_pipe.to(__lowercase		)
										sag_pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				'''.'''
										__lowerCAmelCase							       =				torch.manual_seed(0		)
										__lowerCAmelCase							       =				sag_pipe(
										    [prompt]       ,			width=7_68       ,			height=5_12       ,			generator=__lowercase       ,			guidance_scale=7.5       ,			sag_scale=1.0       ,			num_inference_steps=20       ,			output_type='''np'''       ,			)

										__lowerCAmelCase							       =				output.images

										assert image.shape == (1, 5_12, 7_68, 3)





'''simple docstring'''

from __future__ import annotations

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				sorted(numsa + numsa)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				divmod(len(lowerCamelCase),				2)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCAmelCase				: Union[str, Any]							    =				[float(x) for x in input("""Enter the elements of first array: """).split()]
	_UpperCAmelCase				: int							    =				[float(x) for x in input("""Enter the elements of second array: """).split()]
	print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")

