





'''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 				__snake_case     (					unittest.TestCase							):
	__lowerCAmelCase       						=  StableDiffusionLDMaDPipeline
	__lowerCAmelCase       						=  TEXT_TO_IMAGE_PARAMS
	__lowerCAmelCase       						=  TEXT_TO_IMAGE_BATCH_PARAMS
	__lowerCAmelCase       						=  TEXT_TO_IMAGE_IMAGE_PARAMS





	def   _snake_case   (		self		)      ->		str:
		torch.manual_seed(0		)
		snake_case__	       =   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      ,       )
		snake_case__	       =   DDIMScheduler(
		    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule='scaled_linear'      ,       clip_sample=UpperCamelCase_      ,       set_alpha_to_one=UpperCamelCase_      ,       )
		torch.manual_seed(0		)
		snake_case__	       =   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		)
		snake_case__	       =   CLIPTextConfig(
		    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       )
		snake_case__	       =   CLIPTextModel(UpperCamelCase_		)
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=0		)      ->		Dict:
		if str(UpperCamelCase_		).startswith('mps'		):
			snake_case__	       =   torch.manual_seed(UpperCamelCase_		)
		else:
			snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
		snake_case__	       =   {
		    '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   _snake_case   (		self		)      ->		int:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator

		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   StableDiffusionLDMaDPipeline(**UpperCamelCase_		)
		snake_case__	       =   ldmad_pipe.to(UpperCamelCase_		)
		ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   ldmad_pipe(**UpperCamelCase_		)
		snake_case__       ,  snake_case__	       =   output.rgb, output.depth

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

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

		snake_case__	       =   np.array(
		    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]		)
		snake_case__	       =   np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]		)

		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   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   StableDiffusionLDMaDPipeline(**UpperCamelCase_		)
		snake_case__	       =   ldmad_pipe.to(UpperCamelCase_		)
		ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   3 * [inputs['prompt']]

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

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   3 * [inputs.pop('prompt'		)]

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

		snake_case__	       =   ldmad_pipe.text_encoder(UpperCamelCase_		)[0]

		snake_case__	       =   prompt_embeds

		# forward
		snake_case__	       =   ldmad_pipe(**UpperCamelCase_		)
		snake_case__       ,  snake_case__	       =   output.rgb, output.depth
		snake_case__	       =   rgb_slice_a[0, -3:, -3:, -1]
		snake_case__	       =   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   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
		snake_case__	       =   StableDiffusionLDMaDPipeline(**UpperCamelCase_		)
		snake_case__	       =   ldmad_pipe.to(UpperCamelCase_		)
		ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   'french fries'
		snake_case__	       =   ldmad_pipe(**UpperCamelCase_      ,       negative_prompt=UpperCamelCase_		)

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

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

		snake_case__	       =   np.array(
		    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]		)
		snake_case__	       =   np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]		)
		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 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		str:
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_="cpu"      ,       UpperCamelCase_=torch.floataa      ,       UpperCamelCase_=0		)      ->		List[str]:
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
		snake_case__	       =   np.random.RandomState(UpperCamelCase_		).standard_normal((1, 4, 64, 64)		)
		snake_case__	       =   torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_      ,       dtype=UpperCamelCase_		)
		snake_case__	       =   {
		    '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   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'		)
		snake_case__	       =   ldmad_pipe.to(UpperCamelCase_		)
		ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

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

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

		snake_case__	       =   np.array(
		    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]		)
		snake_case__	       =   np.array(
		    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]		)
		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 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_="cpu"      ,       UpperCamelCase_=torch.floataa      ,       UpperCamelCase_=0		)      ->		int:
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
		snake_case__	       =   np.random.RandomState(UpperCamelCase_		).standard_normal((1, 4, 64, 64)		)
		snake_case__	       =   torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_      ,       dtype=UpperCamelCase_		)
		snake_case__	       =   {
		    '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   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'		).to(UpperCamelCase_		)
		ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_inputs(UpperCamelCase_		)
		snake_case__	       =   ldmad_pipe(**UpperCamelCase_		)
		snake_case__       ,  snake_case__	       =   output.rgb, output.depth

		snake_case__	       =   0.4_9_5_5_8_6
		snake_case__	       =   0.3_3_7_9_5_5_1_5
		snake_case__	       =   1_1_2.4_8_5_1_8
		snake_case__	       =   9_8.4_8_9_7_4_6
		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   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'		).to(UpperCamelCase_		)
		ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_inputs(UpperCamelCase_		)
		snake_case__	       =   ldmad_pipe(**UpperCamelCase_		)
		snake_case__       ,  snake_case__	       =   output.rgb, output.depth

		snake_case__	       =   0.4_1_9_4_1_2_7
		snake_case__	       =   0.3_5_3_7_5_5_8_6
		snake_case__	       =   0.5_6_3_8_5_0_2
		snake_case__	       =   0.3_4_6_8_6_1_0_3

		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__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

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

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


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



