





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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     __magic_name__     (							unittest.TestCase							):





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




        A_    : Optional[int]								=	1
        A_    : List[Any]								=	3
        A_    : str								=	(32, 32)

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



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        torch.manual_seed(0     )
        A_    : List[Any]								=	UNetaDConditionModel(
            block_out_channels=(32, 32, 64)	,					layers_per_block=2	,					sample_size=32	,					in_channels=7	,					out_channels=4	,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")	,					up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")	,					cross_attention_dim=32	,					attention_head_dim=8	,					use_linear_projection=__a	,					only_cross_attention=(True, True, False)	,					num_class_embeds=100	,					)
        return model



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        torch.manual_seed(0     )
        A_    : Any								=	AutoencoderKL(
            block_out_channels=[32, 32, 64]	,					in_channels=3	,					out_channels=3	,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]	,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]	,					latent_channels=4	,					)
        return model



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]     ):





        '''simple docstring'''




        torch.manual_seed(0     )
        A_    : Optional[Any]								=	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=1_000	,					hidden_act="gelu"	,					projection_dim=512	,					)
        return CLIPTextModel(__a     )



     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        A_    : List[str]								=	"cpu"  # ensure determinism for the device-dependent torch.Generator
        A_    : List[Any]								=	self.dummy_cond_unet_upscale
        A_    : int								=	DDPMScheduler()
        A_    : Optional[Any]								=	DDIMScheduler(prediction_type="v_prediction"     )
        A_    : str								=	self.dummy_vae
        A_    : Optional[int]								=	self.dummy_text_encoder
        A_    : Optional[Any]								=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

        A_    : Optional[Any]								=	self.dummy_image.cpu().permute(0	,					2	,					3	,					1     )[0]
        A_    : List[str]								=	Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

        # make sure here that pndm scheduler skips prk
        A_    : str								=	StableDiffusionUpscalePipeline(
            unet=__a	,					low_res_scheduler=__a	,					scheduler=__a	,					vae=__a	,					text_encoder=__a	,					tokenizer=__a	,					max_noise_level=350	,					)
        A_    : Optional[int]								=	sd_pipe.to(__a     )
        sd_pipe.set_progress_bar_config(disable=__a     )

        A_    : List[str]								=	"A painting of a squirrel eating a burger"
        A_    : Dict								=	torch.Generator(device=__a     ).manual_seed(0     )
        A_    : Optional[Any]								=	sd_pipe(
            [prompt]	,					image=__a	,					generator=__a	,					guidance_scale=6.0	,					noise_level=20	,					num_inference_steps=2	,					output_type="np"	,					)

        A_    : Union[str, Any]								=	output.images

        A_    : Tuple								=	torch.Generator(device=__a     ).manual_seed(0     )
        A_    : Union[str, Any]								=	sd_pipe(
            [prompt]	,					image=__a	,					generator=__a	,					guidance_scale=6.0	,					noise_level=20	,					num_inference_steps=2	,					output_type="np"	,					return_dict=__a	,					)[0]

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

        A_    : str								=	low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        A_    : str								=	np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]     )

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



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        A_    : List[str]								=	"cpu"  # ensure determinism for the device-dependent torch.Generator
        A_    : List[Any]								=	self.dummy_cond_unet_upscale
        A_    : Any								=	DDPMScheduler()
        A_    : List[str]								=	DDIMScheduler(prediction_type="v_prediction"     )
        A_    : List[str]								=	self.dummy_vae
        A_    : List[str]								=	self.dummy_text_encoder
        A_    : int								=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

        A_    : List[Any]								=	self.dummy_image.cpu().permute(0	,					2	,					3	,					1     )[0]
        A_    : List[Any]								=	Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

        # make sure here that pndm scheduler skips prk
        A_    : List[Any]								=	StableDiffusionUpscalePipeline(
            unet=__a	,					low_res_scheduler=__a	,					scheduler=__a	,					vae=__a	,					text_encoder=__a	,					tokenizer=__a	,					max_noise_level=350	,					)
        A_    : Optional[int]								=	sd_pipe.to(__a     )
        sd_pipe.set_progress_bar_config(disable=__a     )

        A_    : Tuple								=	"A painting of a squirrel eating a burger"
        A_    : Tuple								=	sd_pipe(
            2 * [prompt]	,					image=2 * [low_res_image]	,					guidance_scale=6.0	,					noise_level=20	,					num_inference_steps=2	,					output_type="np"	,					)
        A_    : Optional[Any]								=	output.images
        assert image.shape[0] == 2

        A_    : List[Any]								=	torch.Generator(device=__a     ).manual_seed(0     )
        A_    : List[Any]								=	sd_pipe(
            [prompt]	,					image=__a	,					generator=__a	,					num_images_per_prompt=2	,					guidance_scale=6.0	,					noise_level=20	,					num_inference_steps=2	,					output_type="np"	,					)
        A_    : Optional[Any]								=	output.images
        assert image.shape[0] == 2





     @unittest.skipIf(torch_device != "cuda"	,					"This test requires a GPU"     )
     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




        A_    : Dict								=	self.dummy_cond_unet_upscale
        A_    : Optional[int]								=	DDPMScheduler()
        A_    : str								=	DDIMScheduler(prediction_type="v_prediction"     )
        A_    : str								=	self.dummy_vae
        A_    : Dict								=	self.dummy_text_encoder
        A_    : Union[str, Any]								=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

        A_    : Union[str, Any]								=	self.dummy_image.cpu().permute(0	,					2	,					3	,					1     )[0]
        A_    : int								=	Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

        # put models in fp16, except vae as it overflows in fp16
        A_    : Any								=	unet.half()
        A_    : Optional[Any]								=	text_encoder.half()

        # make sure here that pndm scheduler skips prk
        A_    : int								=	StableDiffusionUpscalePipeline(
            unet=__a	,					low_res_scheduler=__a	,					scheduler=__a	,					vae=__a	,					text_encoder=__a	,					tokenizer=__a	,					max_noise_level=350	,					)
        A_    : Union[str, Any]								=	sd_pipe.to(__a     )
        sd_pipe.set_progress_bar_config(disable=__a     )

        A_    : List[Any]								=	"A painting of a squirrel eating a burger"
        A_    : List[str]								=	torch.manual_seed(0     )
        A_    : Optional[Any]								=	sd_pipe(
            [prompt]	,					image=__a	,					generator=__a	,					num_inference_steps=2	,					output_type="np"	,					).images

        A_    : Dict								=	low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)




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





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : Optional[int]								=	load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"     )
        A_    : Any								=	load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
            "/upsampled_cat.npy"     )

        A_    : Dict								=	"stabilityai/stable-diffusion-x4-upscaler"
        A_    : Optional[int]								=	StableDiffusionUpscalePipeline.from_pretrained(__a     )
        pipe.to(__a     )
        pipe.set_progress_bar_config(disable=__a     )
        pipe.enable_attention_slicing()

        A_    : Any								=	"a cat sitting on a park bench"

        A_    : Union[str, Any]								=	torch.manual_seed(0     )
        A_    : Tuple								=	pipe(
            prompt=__a	,					image=__a	,					generator=__a	,					output_type="np"	,					)
        A_    : Union[str, Any]								=	output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image     ).max() < 1e-3



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        A_    : int								=	load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"     )
        A_    : Optional[int]								=	load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
            "/upsampled_cat_fp16.npy"     )

        A_    : Optional[int]								=	"stabilityai/stable-diffusion-x4-upscaler"
        A_    : str								=	StableDiffusionUpscalePipeline.from_pretrained(
            __a	,					torch_dtype=torch.floataa	,					)
        pipe.to(__a     )
        pipe.set_progress_bar_config(disable=__a     )
        pipe.enable_attention_slicing()

        A_    : Tuple								=	"a cat sitting on a park bench"

        A_    : str								=	torch.manual_seed(0     )
        A_    : Union[str, Any]								=	pipe(
            prompt=__a	,					image=__a	,					generator=__a	,					output_type="np"	,					)
        A_    : str								=	output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image     ).max() < 5e-1





     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A_    : str								=	load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"     )

        A_    : int								=	"stabilityai/stable-diffusion-x4-upscaler"
        A_    : List[str]								=	StableDiffusionUpscalePipeline.from_pretrained(
            __a	,					torch_dtype=torch.floataa	,					)
        pipe.to(__a     )
        pipe.set_progress_bar_config(disable=__a     )
        pipe.enable_attention_slicing(1     )
        pipe.enable_sequential_cpu_offload()

        A_    : List[Any]								=	"a cat sitting on a park bench"

        A_    : str								=	torch.manual_seed(0     )
        A_    : Optional[Any]								=	pipe(
            prompt=__a	,					image=__a	,					generator=__a	,					num_inference_steps=5	,					output_type="np"	,					)

        A_    : Dict								=	torch.cuda.max_memory_allocated()
        # make sure that less than 2.9 GB is allocated
        assert mem_bytes < 2.9 * 10**9

'''simple docstring'''





import os
from math import logaa





def 	lowerCAmelCase_				(					snake_case_       : str = "base_exp.txt"			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0
     for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_			)					,    snake_case_			)			)			):
          UpperCAmelCase_    , UpperCAmelCase_           =   list(map(snake_case_					,    line.split(","			)			)			)
          if x * logaa(snake_case_			) > largest:
               UpperCAmelCase_           =   x * logaa(snake_case_			)
               UpperCAmelCase_           =   i + 1
     return result


if __name__ == "__main__":
    print(solution())




