






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		Dict		)  ->  Any:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		Any		)  ->  List[str]:


       '''simple docstring'''

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

       lowercase					:   List[Any]				  =floats_tensor((batch_size, num_channels) + sizes     ,      rng=random.Random(0		)		).to(UpperCAmelCase		)
       return image






      @property
      def 				A__      ( self			:		Tuple		)  ->  str:


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase					:   Optional[int]				  =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     ,      )
       return model






      @property
      def 				A__      ( self			:		Tuple		)  ->  List[str]:


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase					:   int				  =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     ,      )
       return model






      @property
      def 				A__      ( self			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase					:   Dict				  =RobertaSeriesConfig(
           hidden_size=32     ,      project_dim=32     ,      intermediate_size=37     ,      layer_norm_eps=1e-05     ,      num_attention_heads=4     ,      num_hidden_layers=5     ,      pad_token_id=1     ,      vocab_size=5006     ,      )
       return RobertaSeriesModelWithTransformation(UpperCAmelCase		)






      @property
      def 				A__      ( self			:		Optional[int]		)  ->  Dict:


       '''simple docstring'''

       def extract(*UpperCAmelCase			:		Optional[int]     ,      **UpperCAmelCase			:		Optional[Any]		):
        class UpperCAmelCase_					:




              """simple docstring"""




              def __init__( self			:		int		)  ->  str:


               '''simple docstring'''

               lowercase					:   Any				  =torch.ones([0]		)






              def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		List[Any]		)  ->  Optional[Any]:


               '''simple docstring'''

               self.pixel_values.to(UpperCAmelCase		)
               return self





        return Out()

       return extract






      def 				A__      ( self			:		Any		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       lowercase					:   Dict				  =self.dummy_cond_unet
       lowercase					:   Dict				  =PNDMScheduler(skip_prk_steps=UpperCAmelCase		)
       lowercase					:   str				  =self.dummy_vae
       lowercase					:   str				  =self.dummy_text_encoder
       lowercase					:   Dict				  =XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
       lowercase					:   str				  =77

       lowercase					:   Any				  =self.dummy_image.to(UpperCAmelCase		)
       lowercase					:   Dict				  =init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       lowercase					:   Union[str, Any]				  =AltDiffusionImgaImgPipeline(
           unet=UpperCAmelCase     ,      scheduler=UpperCAmelCase     ,      vae=UpperCAmelCase     ,      text_encoder=UpperCAmelCase     ,      tokenizer=UpperCAmelCase     ,      safety_checker=UpperCAmelCase     ,      feature_extractor=self.dummy_extractor     ,      )
       lowercase					:   Optional[int]				  =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor     ,      do_normalize=UpperCAmelCase		)
       lowercase					:   Any				  =alt_pipe.to(UpperCAmelCase		)
       alt_pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase					:   List[Any]				  ='''A painting of a squirrel eating a burger'''
       lowercase					:   List[Any]				  =torch.Generator(device=UpperCAmelCase		).manual_seed(0		)
       lowercase					:   Optional[int]				  =alt_pipe(
           [prompt]     ,      generator=UpperCAmelCase     ,      guidance_scale=6.0     ,      num_inference_steps=2     ,      output_type='''np'''     ,      image=UpperCAmelCase     ,      )

       lowercase					:   List[str]				  =output.images

       lowercase					:   Any				  =torch.Generator(device=UpperCAmelCase		).manual_seed(0		)
       lowercase					:   Dict				  =alt_pipe(
           [prompt]     ,      generator=UpperCAmelCase     ,      guidance_scale=6.0     ,      num_inference_steps=2     ,      output_type='''np'''     ,      image=UpperCAmelCase     ,      return_dict=UpperCAmelCase     ,      )[0]

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

       assert image.shape == (1, 32, 32, 3)
       lowercase					:   Any				  =np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5e-3






      @unittest.skipIf(torch_device != '''cuda'''     ,      '''This test requires a GPU'''		)
      def 				A__      ( self			:		List[str]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Dict				  =self.dummy_cond_unet
       lowercase					:   int				  =PNDMScheduler(skip_prk_steps=UpperCAmelCase		)
       lowercase					:   List[str]				  =self.dummy_vae
       lowercase					:   List[str]				  =self.dummy_text_encoder
       lowercase					:   int				  =XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
       lowercase					:   str				  =77

       lowercase					:   Dict				  =self.dummy_image.to(UpperCAmelCase		)

       # put models in fp16
       lowercase					:   Union[str, Any]				  =unet.half()
       lowercase					:   Dict				  =vae.half()
       lowercase					:   Optional[Any]				  =bert.half()

       # make sure here that pndm scheduler skips prk
       lowercase					:   Union[str, Any]				  =AltDiffusionImgaImgPipeline(
           unet=UpperCAmelCase     ,      scheduler=UpperCAmelCase     ,      vae=UpperCAmelCase     ,      text_encoder=UpperCAmelCase     ,      tokenizer=UpperCAmelCase     ,      safety_checker=UpperCAmelCase     ,      feature_extractor=self.dummy_extractor     ,      )
       lowercase					:   Dict				  =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor     ,      do_normalize=UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =alt_pipe.to(UpperCAmelCase		)
       alt_pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase					:   str				  ='''A painting of a squirrel eating a burger'''
       lowercase					:   Optional[int]				  =torch.manual_seed(0		)
       lowercase					:   Any				  =alt_pipe(
           [prompt]     ,      generator=UpperCAmelCase     ,      num_inference_steps=2     ,      output_type='''np'''     ,      image=UpperCAmelCase     ,      ).images

       assert image.shape == (1, 32, 32, 3)






      @unittest.skipIf(torch_device != '''cuda'''     ,      '''This test requires a GPU'''		)
      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Dict				  =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''		)
       # resize to resolution that is divisible by 8 but not 16 or 32
       lowercase					:   Optional[Any]				  =init_image.resize((760, 504)		)

       lowercase					:   Optional[Any]				  ='''BAAI/AltDiffusion'''
       lowercase					:   Union[str, Any]				  =AltDiffusionImgaImgPipeline.from_pretrained(
           UpperCAmelCase     ,      safety_checker=UpperCAmelCase     ,      )
       pipe.to(UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=UpperCAmelCase		)
       pipe.enable_attention_slicing()

       lowercase					:   List[str]				  ='''A fantasy landscape, trending on artstation'''

       lowercase					:   str				  =torch.manual_seed(0		)
       lowercase					:   List[str]				  =pipe(
           prompt=UpperCAmelCase     ,      image=UpperCAmelCase     ,      strength=0.7_5     ,      guidance_scale=7.5     ,      generator=UpperCAmelCase     ,      output_type='''np'''     ,      )
       lowercase					:   List[str]				  =output.images[0]

       lowercase					:   List[Any]				  =image[255:258, 383:386, -1]

       assert image.shape == (504, 760, 3)
       lowercase					:   List[Any]				  =np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]		)

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





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




      """simple docstring"""




      def 				A__      ( self			:		int		)  ->  int:


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''		)
       lowercase					:   Dict				  =init_image.resize((768, 512)		)
       lowercase					:   Dict				  =load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''		)

       lowercase					:   Any				  ='''BAAI/AltDiffusion'''
       lowercase					:   Any				  =AltDiffusionImgaImgPipeline.from_pretrained(
           UpperCAmelCase     ,      safety_checker=UpperCAmelCase     ,      )
       pipe.to(UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=UpperCAmelCase		)
       pipe.enable_attention_slicing()

       lowercase					:   Tuple				  ='''A fantasy landscape, trending on artstation'''

       lowercase					:   Union[str, Any]				  =torch.manual_seed(0		)
       lowercase					:   Union[str, Any]				  =pipe(
           prompt=UpperCAmelCase     ,      image=UpperCAmelCase     ,      strength=0.7_5     ,      guidance_scale=7.5     ,      generator=UpperCAmelCase     ,      output_type='''np'''     ,      )
       lowercase					:   List[Any]				  =output.images[0]

       assert image.shape == (512, 768, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image		).max() < 1e-2











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




 lowercase					:   int				  ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

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

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


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



