






"""simple docstring"""





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()
class    __lowerCamelCase    (  unittest.TestCase  ):
   a      :				Optional[Any] 						=StableDiffusionLDMaDPipeline
   a      :				int 						=TEXT_TO_IMAGE_PARAMS
   a      :				Tuple 						=TEXT_TO_IMAGE_BATCH_PARAMS
   a      :				List[str] 						=TEXT_TO_IMAGE_IMAGE_PARAMS

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       torch.manual_seed(0      )
       UpperCamelCase__         =    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 ,				)
       UpperCamelCase__         =    DDIMScheduler(
           beta_start=0.00_085 ,				beta_end=0.012 ,				beta_schedule='scaled_linear' ,				clip_sample=snake_case_ ,				set_alpha_to_one=snake_case_ ,				)
       torch.manual_seed(0      )
       UpperCamelCase__         =    AutoencoderKL(
           block_out_channels=[32, 64] ,				in_channels=6 ,				out_channels=6 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=4 ,				)
       torch.manual_seed(0      )
       UpperCamelCase__         =    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 ,				)
       UpperCamelCase__         =    CLIPTextModel(snake_case_      )
       UpperCamelCase__         =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=0      )		->       Dict:
       if str(snake_case_      ).startswith('mps'      ):
           UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       else:
           UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    {
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator

       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionLDMaDPipeline(**snake_case_      )
       UpperCamelCase__         =    ldmad_pipe.to(snake_case_      )
       ldmad_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    ldmad_pipe(**snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth

       UpperCamelCase__         =    rgb[0, -3:, -3:, -1]
       UpperCamelCase__         =    depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       UpperCamelCase__         =    np.array(
           [0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262]      )
       UpperCamelCase__         =    np.array([103.46_727, 85.812_004, 87.849_236]      )

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb      ).max() < 1E-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth      ).max() < 1E-2

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionLDMaDPipeline(**snake_case_      )
       UpperCamelCase__         =    ldmad_pipe.to(snake_case_      )
       ldmad_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    3 * [inputs['prompt']]

       # forward
       UpperCamelCase__         =    ldmad_pipe(**snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth
       UpperCamelCase__         =    rgb_slice_a[0, -3:, -3:, -1]
       UpperCamelCase__         =    depth_slice_a[0, -3:, -1]

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    3 * [inputs.pop('prompt'      )]

       UpperCamelCase__         =    ldmad_pipe.tokenizer(
           snake_case_ ,				padding='max_length' ,				max_length=ldmad_pipe.tokenizer.model_max_length ,				truncation=snake_case_ ,				return_tensors='pt' ,				)
       UpperCamelCase__         =    text_inputs['input_ids'].to(snake_case_      )

       UpperCamelCase__         =    ldmad_pipe.text_encoder(snake_case_      )[0]

       UpperCamelCase__         =    prompt_embeds

       # forward
       UpperCamelCase__         =    ldmad_pipe(**snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth
       UpperCamelCase__         =    rgb_slice_a[0, -3:, -3:, -1]
       UpperCamelCase__         =    depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()      ).max() < 1E-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()      ).max() < 1E-4







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    PNDMScheduler(skip_prk_steps=snake_case_      )
       UpperCamelCase__         =    StableDiffusionLDMaDPipeline(**snake_case_      )
       UpperCamelCase__         =    ldmad_pipe.to(snake_case_      )
       ldmad_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    'french fries'
       UpperCamelCase__         =    ldmad_pipe(**snake_case_ ,				negative_prompt=snake_case_      )

       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth
       UpperCamelCase__         =    rgb[0, -3:, -3:, -1]
       UpperCamelCase__         =    depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       UpperCamelCase__         =    np.array(
           [0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217]      )
       UpperCamelCase__         =    np.array([107.84_738, 84.62_802, 89.962_135]      )
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb      ).max() < 1E-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth      ).max() < 1E-2







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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_="cpu" ,				snake_case_=torch.floataa ,				snake_case_=0      )		->       int:
       UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    np.random.RandomState(snake_case_      ).standard_normal((1, 4, 64, 64)      )
       UpperCamelCase__         =    torch.from_numpy(snake_case_      ).to(device=snake_case_ ,				dtype=snake_case_      )
       UpperCamelCase__         =    {
           'prompt': 'a photograph of an astronaut riding a horse',
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'      )
       UpperCamelCase__         =    ldmad_pipe.to(snake_case_      )
       ldmad_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_inputs(snake_case_      )
       UpperCamelCase__         =    ldmad_pipe(**snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth
       UpperCamelCase__         =    rgb[0, -3:, -3:, -1].flatten()
       UpperCamelCase__         =    rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       UpperCamelCase__         =    np.array(
           [0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706]      )
       UpperCamelCase__         =    np.array(
           [0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706]      )
       assert np.abs(rgb_slice - expected_slice_rgb      ).max() < 3E-3
       assert np.abs(depth_slice - expected_slice_depth      ).max() < 3E-3







@nightly
@require_torch_gpu
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_="cpu" ,				snake_case_=torch.floataa ,				snake_case_=0      )		->       Optional[int]:
       UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    np.random.RandomState(snake_case_      ).standard_normal((1, 4, 64, 64)      )
       UpperCamelCase__         =    torch.from_numpy(snake_case_      ).to(device=snake_case_ ,				dtype=snake_case_      )
       UpperCamelCase__         =    {
           'prompt': 'a photograph of an astronaut riding a horse',
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 50,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'      ).to(snake_case_      )
       ldmad_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_inputs(snake_case_      )
       UpperCamelCase__         =    ldmad_pipe(**snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth

       UpperCamelCase__         =    0.495_586
       UpperCamelCase__         =    0.33_795_515
       UpperCamelCase__         =    112.48_518
       UpperCamelCase__         =    98.489_746
       assert np.abs(expected_rgb_mean - rgb.mean()      ) < 1E-3
       assert np.abs(expected_rgb_std - rgb.std()      ) < 1E-3
       assert np.abs(expected_depth_mean - depth.mean()      ) < 1E-3
       assert np.abs(expected_depth_std - depth.std()      ) < 1E-3







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'      ).to(snake_case_      )
       ldmad_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_inputs(snake_case_      )
       UpperCamelCase__         =    ldmad_pipe(**snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    output.rgb, output.depth

       UpperCamelCase__         =    0.4_194_127
       UpperCamelCase__         =    0.35_375_586
       UpperCamelCase__         =    0.5_638_502
       UpperCamelCase__         =    0.34_686_103

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()      ) < 1E-3
       assert np.abs(expected_rgb_std - rgb.std()      ) < 1E-3
       assert np.abs(expected_depth_mean - depth.mean()      ) < 1E-3
       assert np.abs(expected_depth_std - depth.std()      ) < 1E-3












"""simple docstring"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

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

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(SCREAMING_SNAKE_CASE					)					)
        elif i in operators:
            # RULE 2
            operator_stack.push(SCREAMING_SNAKE_CASE					)
        elif i == ")":
            # RULE 4
            UpperCamelCase__         =    operator_stack.peek()
            operator_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  A__					: int=	"""(5 + ((4 * 2) * (2 + 3)))"""
  # answer = 45
  print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")




