






"""simple docstring"""
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 _lowerCAmelCase						(    unittest.TestCase       ):







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







						@property
						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Any       		=    1
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    3
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    (3_2, 3_2)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    floats_tensor((batch_size, num_channels) + sizes   ,				rng=random.Random(0							)							).to(lowerCAmelCase_							)
										return image







						@property
						def 			A      (    self							)		->							int:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    UNetaDConditionModel(
										    block_out_channels=(3_2, 3_2, 6_4)   ,				layers_per_block=2   ,				sample_size=3_2   ,				in_channels=7   ,				out_channels=4   ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')   ,				up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')   ,				cross_attention_dim=3_2   ,				attention_head_dim=8   ,				use_linear_projection=lowerCAmelCase_   ,				only_cross_attention=(True, True, False)   ,				num_class_embeds=1_0_0   ,				)
										return model







						@property
						def 			A      (    self							)		->							int:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoencoderKL(
										    block_out_channels=[3_2, 3_2, 6_4]   ,				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 			A      (    self							)		->							Tuple:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    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   ,				hidden_act='gelu'   ,				projection_dim=5_1_2   ,				)
										return CLIPTextModel(lowerCAmelCase_							)







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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.dummy_image.cpu().permute(0   ,				2   ,				3   ,				1							)[0]
										_SCREAMING_SNAKE_CASE   :		int       		=    Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    StableDiffusionUpscalePipeline(
										    unet=lowerCAmelCase_   ,				low_res_scheduler=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				max_noise_level=3_5_0   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe(
										    [prompt]   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				guidance_scale=6.0   ,				noise_level=2_0   ,				num_inference_steps=2   ,				output_type='np'   ,				)

										_SCREAMING_SNAKE_CASE   :		str       		=    output.images

										_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    sd_pipe(
										    [prompt]   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				guidance_scale=6.0   ,				noise_level=2_0   ,				num_inference_steps=2   ,				output_type='np'   ,				return_dict=lowerCAmelCase_   ,				)[0]

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

										_SCREAMING_SNAKE_CASE   :		Any       		=    low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]							)

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

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.dummy_image.cpu().permute(0   ,				2   ,				3   ,				1							)[0]
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE   :		Dict       		=    StableDiffusionUpscalePipeline(
										    unet=lowerCAmelCase_   ,				low_res_scheduler=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				max_noise_level=3_5_0   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe(
										    2 * [prompt]   ,				image=2 * [low_res_image]   ,				guidance_scale=6.0   ,				noise_level=2_0   ,				num_inference_steps=2   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    output.images
										assert image.shape[0] == 2

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe(
										    [prompt]   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_images_per_prompt=2   ,				guidance_scale=6.0   ,				noise_level=2_0   ,				num_inference_steps=2   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    output.images
										assert image.shape[0] == 2







						@unittest.skipIf(torch_device != 'cuda'   ,				'This test requires a GPU'							)
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.dummy_cond_unet_upscale
										_SCREAMING_SNAKE_CASE   :		str       		=    DDPMScheduler()
										_SCREAMING_SNAKE_CASE   :		Dict       		=    DDIMScheduler(prediction_type='v_prediction'							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.dummy_vae
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.dummy_image.cpu().permute(0   ,				2   ,				3   ,				1							)[0]
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

										# put models in fp16, except vae as it overflows in fp16
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    unet.half()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    text_encoder.half()

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    StableDiffusionUpscalePipeline(
										    unet=lowerCAmelCase_   ,				low_res_scheduler=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				max_noise_level=3_5_0   ,				)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe(
										    [prompt]   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=2   ,				output_type='np'   ,				).images

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)







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







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







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-upscale/low_res_cat.png'							)
										_SCREAMING_SNAKE_CASE   :		str       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
										    '/upsampled_cat.npy'							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'stabilityai/stable-diffusion-x4-upscaler'
										_SCREAMING_SNAKE_CASE   :		Dict       		=    StableDiffusionUpscalePipeline.from_pretrained(lowerCAmelCase_							)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

										_SCREAMING_SNAKE_CASE   :		Dict       		=    'a cat sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)
										assert np.abs(expected_image - image							).max() < 1e-3







						def 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		Any       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-upscale/low_res_cat.png'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
										    '/upsampled_cat_fp16.npy'							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'stabilityai/stable-diffusion-x4-upscaler'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionUpscalePipeline.from_pretrained(
										    lowerCAmelCase_   ,				torch_dtype=torch.floataa   ,				)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'a cat sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)
										assert np.abs(expected_image - image							).max() < 5e-1







						def 			A      (    self							)		->							Optional[Any]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-upscale/low_res_cat.png'							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    'stabilityai/stable-diffusion-x4-upscaler'
										_SCREAMING_SNAKE_CASE   :		Dict       		=    StableDiffusionUpscalePipeline.from_pretrained(
										    lowerCAmelCase_   ,				torch_dtype=torch.floataa   ,				)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing(1							)
										pipe.enable_sequential_cpu_offload()

										_SCREAMING_SNAKE_CASE   :		str       		=    'a cat sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=5   ,				output_type='np'   ,				)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.cuda.max_memory_allocated()
										# make sure that less than 2.9 GB is allocated
										assert mem_bytes < 2.9 * 1_0**9









"""simple docstring"""
import operator as op





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    []
				_SCREAMING_SNAKE_CASE   :		Dict       		=    lambda lowerCamelCase,						lowerCamelCase       : int(x / y       )  # noqa: E731 integer division operation
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8       ),						'Action'.center(12       ),						'Stack',						sep=' | '       )
				print('-' * (30 + len(lowerCamelCase       ))       )

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowerCamelCase       )  # append x to stack
												# output in tabular format
												print(x.rjust(8       ),						('push(' + x + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )
								else:
												_SCREAMING_SNAKE_CASE   :		int       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												_SCREAMING_SNAKE_CASE   :		Tuple       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + a + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												stack.append(
												    str(opr[x](int(lowerCamelCase       ),						int(lowerCamelCase       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8       ),						('push(' + a + x + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | ',						)

				return int(stack[0]       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
			print('\n\tResult = ', solve(Postfix))

