


'''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	):
    """simple docstring"""


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


    @property
    def        A   (  self      :							Dict):
           _A					:     List[str]     = 1
           _A					:     List[str]     = 3
           _A					:     str     = (32, 32)

           _A					:     Union[str, Any]     = floats_tensor((batch_size, num_channels) + sizes				,							rng=random.Random(0)).to(SCREAMING_SNAKE_CASE)
           return image


    @property
    def        A   (  self      :							Optional[int]):
           torch.manual_seed(0)
           _A					:     List[str]     = 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=SCREAMING_SNAKE_CASE				,							only_cross_attention=(True, True, False)				,							num_class_embeds=100				,							)
           return model


    @property
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     List[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        A   (  self      :							Union[str, Any]):
           torch.manual_seed(0)
           _A					:     List[str]     = 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=1000				,							hidden_act='gelu'				,							projection_dim=512				,							)
           return CLIPTextModel(SCREAMING_SNAKE_CASE)


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

           _A					:     List[str]     = self.dummy_image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     str     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))

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

           _A					:     Tuple     = 'A painting of a squirrel eating a burger'
           _A					:     Union[str, Any]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
           _A					:     str     = sd_pipe(
               [prompt]				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							guidance_scale=6.0				,							noise_level=20				,							num_inference_steps=2				,							output_type='np'				,							)

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

           _A					:     Any     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
           _A					:     Any     = sd_pipe(
               [prompt]				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							guidance_scale=6.0				,							noise_level=20				,							num_inference_steps=2				,							output_type='np'				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

           _A					:     List[Any]     = image[0, -3:, -3:, -1]
           _A					:     List[Any]     = image_from_tuple[0, -3:, -3:, -1]

           _A					:     Optional[Any]     = low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           _A					:     Optional[int]     = 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        A   (  self      :							Union[str, Any]):
           _A					:     Optional[int]     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
           _A					:     List[str]     = self.dummy_cond_unet_upscale
           _A					:     str     = DDPMScheduler()
           _A					:     List[str]     = DDIMScheduler(prediction_type='v_prediction')
           _A					:     int     = self.dummy_vae
           _A					:     Optional[Any]     = self.dummy_text_encoder
           _A					:     Optional[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

           # make sure here that pndm scheduler skips prk
           _A					:     Any     = StableDiffusionUpscalePipeline(
               unet=SCREAMING_SNAKE_CASE				,							low_res_scheduler=SCREAMING_SNAKE_CASE				,							scheduler=SCREAMING_SNAKE_CASE				,							vae=SCREAMING_SNAKE_CASE				,							text_encoder=SCREAMING_SNAKE_CASE				,							tokenizer=SCREAMING_SNAKE_CASE				,							max_noise_level=350				,							)
           _A					:     Tuple     = sd_pipe.to(SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = 'A painting of a squirrel eating a burger'
           _A					:     Union[str, Any]     = 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					:     Optional[int]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(0)
           _A					:     Any     = sd_pipe(
               [prompt]				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							num_images_per_prompt=2				,							guidance_scale=6.0				,							noise_level=20				,							num_inference_steps=2				,							output_type='np'				,							)
           _A					:     Optional[int]     = output.images
           assert image.shape[0] == 2


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

           _A					:     Tuple     = self.dummy_image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Optional[int]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))

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

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

           _A					:     Any     = 'A painting of a squirrel eating a burger'
           _A					:     Dict     = torch.manual_seed(0)
           _A					:     int     = sd_pipe(
               [prompt]				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							num_inference_steps=2				,							output_type='np'				,							).images

           _A					:     Optional[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	):
    """simple docstring"""


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


    def        A   (  self      :							str):
           _A					:     str     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/sd2-upscale/low_res_cat.png')
           _A					:     Dict     = 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					:     Tuple     = StableDiffusionUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           _A					:     List[str]     = 'a cat sitting on a park bench'

           _A					:     List[Any]     = torch.manual_seed(0)
           _A					:     Optional[int]     = pipe(
               prompt=SCREAMING_SNAKE_CASE				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np'				,							)
           _A					:     List[Any]     = output.images[0]

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


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

           _A					:     Any     = 'stabilityai/stable-diffusion-x4-upscaler'
           _A					:     str     = StableDiffusionUpscalePipeline.from_pretrained(
               SCREAMING_SNAKE_CASE				,							torch_dtype=torch.floataa				,							)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           _A					:     Union[str, Any]     = 'a cat sitting on a park bench'

           _A					:     Any     = torch.manual_seed(0)
           _A					:     Any     = pipe(
               prompt=SCREAMING_SNAKE_CASE				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np'				,							)
           _A					:     List[Any]     = output.images[0]

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


    def        A   (  self      :							List[str]):
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           _A					:     Dict     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/sd2-upscale/low_res_cat.png')

           _A					:     Optional[int]     = 'stabilityai/stable-diffusion-x4-upscaler'
           _A					:     str     = StableDiffusionUpscalePipeline.from_pretrained(
               SCREAMING_SNAKE_CASE				,							torch_dtype=torch.floataa				,							)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

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

           _A					:     Any     = torch.manual_seed(0)
           _A					:     str     = pipe(
               prompt=SCREAMING_SNAKE_CASE				,							image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							num_inference_steps=5				,							output_type='np'				,							)

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










'''simple docstring'''







A				:					Dict    						=			'''Alexander Joslin'''

import operator as op

from .stack import Stack






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     Dict     = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

       _A					:     Stack[int]     = Stack()
       _A					:     Stack[str]     = Stack()

       for i in equation:
              if i.isdigit():
                     # RULE 1
                     operand_stack.push(int(lowerCamelCase       )       )
              elif i in operators:
                     # RULE 2
                     operator_stack.push(lowerCamelCase       )
              elif i == ")":
                     # RULE 4
                     _A					:     List[str]     = operator_stack.peek()
                     operator_stack.pop()
                     _A					:     Dict     = operand_stack.peek()
                     operand_stack.pop()
                     _A					:     Optional[int]     = operand_stack.peek()
                     operand_stack.pop()

                     _A					:     Dict     = operators[opr](lowerCamelCase  ,lowerCamelCase       )
                     operand_stack.push(lowerCamelCase       )

    # RULE 5
       return operand_stack.peek()


if __name__ == "__main__":
   A				:					Dict    						=			'''(5 + ((4 * 2) * (2 + 3)))'''
   # answer = 45
   print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






