

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A											=     logging.get_logger(__name__)  # pylint: disable=invalid-name

A											=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def a(lowercase__			, lowercase__			, lowercase__=8	):





	'''simple docstring'''
	snake_case_      		= height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	snake_case_      		= width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor





def a(lowercase__			, lowercase__=512			, lowercase__=512	):





	'''simple docstring'''
	snake_case_      		= pil_image.resize((w, h)			, resample=Image.BICUBIC			, reducing_gap=1	)
	snake_case_      		= np.array(pil_image.convert('RGB'	)	)
	snake_case_      		= arr.astype(np.floataa	) / 127.5 - 1
	snake_case_      		= np.transpose(lowercase__			, [2, 0, 1]	)
	snake_case_      		= torch.from_numpy(lowercase__	).unsqueeze(0	)
	return image






class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

				self.register_modules(
				    unet=__UpperCamelCase ,				scheduler=__UpperCamelCase ,				movq=__UpperCamelCase ,				)
				snake_case_      		= 2 ** (len(self.movq.config.block_out_channels							) - 1)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= min(int(num_inference_steps * strength							) ,				__UpperCamelCase							)

				snake_case_      		= max(num_inference_steps - init_timestep ,				0							)
				snake_case_      		= self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=None							):

				"""simple docstring"""


				if not isinstance(__UpperCamelCase ,				(torch.Tensor, PIL.Image.Image, list)							):
					raise ValueError(
					    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCamelCase							)}"""							)

				snake_case_      		= image.to(device=__UpperCamelCase ,				dtype=__UpperCamelCase							)

				snake_case_      		= batch_size * num_images_per_prompt

				if image.shape[1] == 4:
					snake_case_      		= image

				else:
					if isinstance(__UpperCamelCase ,				__UpperCamelCase							) and len(__UpperCamelCase							) != batch_size:
						raise ValueError(
						    f"""You have passed a list of generators of length {len(__UpperCamelCase							)}, but requested an effective batch"""
						    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

					elif isinstance(__UpperCamelCase ,				__UpperCamelCase							):
						snake_case_      		= [
						    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(__UpperCamelCase							)
						]
						snake_case_      		= torch.cat(__UpperCamelCase ,				dim=0							)
					else:
						snake_case_      		= self.movq.encode(__UpperCamelCase							).latent_dist.sample(__UpperCamelCase							)

					snake_case_      		= self.movq.config.scaling_factor * init_latents

				snake_case_      		= torch.cat([init_latents] ,				dim=0							)

				snake_case_      		= init_latents.shape
				snake_case_      		= randn_tensor(__UpperCamelCase ,				generator=__UpperCamelCase ,				device=__UpperCamelCase ,				dtype=__UpperCamelCase							)

				# get latents
				snake_case_      		= self.scheduler.add_noise(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= init_latents

				return latents


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0							):

				"""simple docstring"""


				if is_accelerate_available():
					from accelerate import cpu_offload
				else:
					raise ImportError('Please install accelerate via `pip install accelerate`'							)

				snake_case_      		= torch.device(f"""cuda:{gpu_id}"""							)

				snake_case_      		= [
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
						cpu_offload(__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0							):

				"""simple docstring"""


				if is_accelerate_available() and is_accelerate_version('>=' ,				'0.17.0.dev0'							):
					from accelerate import cpu_offload_with_hook
				else:
					raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'							)

				snake_case_      		= torch.device(f"""cuda:{gpu_id}"""							)

				if self.device.type != "cpu":
					self.to('cpu' ,				silence_dtype_warnings=__UpperCamelCase							)
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

				snake_case_      		= None
				for cpu_offloaded_model in [self.unet, self.movq]:
					snake_case_		,       snake_case_      		= cpu_offload_with_hook(__UpperCamelCase ,				__UpperCamelCase ,				prev_module_hook=__UpperCamelCase							)

				# We'll offload the last model manually.
				snake_case_      		= hook


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if not hasattr(self.unet ,				'_hf_hook'							):
					return self.device
				for module in self.unet.modules():
					if (
					    hasattr(__UpperCamelCase ,				'_hf_hook'							)
					    and hasattr(module._hf_hook ,				'execution_device'							)
					    and module._hf_hook.execution_device is not None
					):
						return torch.device(module._hf_hook.execution_device							)
				return self.device







			@torch.no_grad()
			@replace_example_docstring(__UpperCamelCase							)
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 5_12 ,				__UpperCamelCase = 5_12 ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 4.0 ,				__UpperCamelCase = 0.3 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				):

				"""simple docstring"""


				snake_case_      		= self._execution_device

				snake_case_      		= guidance_scale > 1.0

				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= torch.cat(__UpperCamelCase ,				dim=0							)
				snake_case_      		= image_embeds.shape[0]
				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= torch.cat(__UpperCamelCase ,				dim=0							)

				if do_classifier_free_guidance:
					snake_case_      		= image_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)
					snake_case_      		= negative_image_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)

				snake_case_      		= torch.cat([negative_image_embeds, image_embeds] ,				dim=0							).to(dtype=self.unet.dtype ,				device=__UpperCamelCase							)

				if not isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= [image]
				if not all(isinstance(__UpperCamelCase ,				(PIL.Image.Image, torch.Tensor)							) for i in image							):
					raise ValueError(
					    f"""Input is in incorrect format: {[type(__UpperCamelCase							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

				snake_case_      		= torch.cat([prepare_image(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							) for i in image] ,				dim=0							)
				snake_case_      		= image.to(dtype=image_embeds.dtype ,				device=__UpperCamelCase							)

				snake_case_      		= self.movq.encode(__UpperCamelCase							)['latents']
				snake_case_      		= latents.repeat_interleave(__UpperCamelCase ,				dim=0							)
				self.scheduler.set_timesteps(__UpperCamelCase ,				device=__UpperCamelCase							)
				snake_case_		,       snake_case_      		= self.get_timesteps(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)
				snake_case_      		= timesteps[:1].repeat(batch_size * num_images_per_prompt							)
				snake_case_		,       snake_case_      		= downscale_height_and_width(__UpperCamelCase ,				__UpperCamelCase ,				self.movq_scale_factor							)
				snake_case_      		= self.prepare_latents(
				    __UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				image_embeds.dtype ,				__UpperCamelCase ,				__UpperCamelCase							)
				for i, t in enumerate(self.progress_bar(__UpperCamelCase							)							):
					# expand the latents if we are doing classifier free guidance
					snake_case_      		= torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

					snake_case_      		= {'image_embeds': image_embeds}
					snake_case_      		= self.unet(
					    sample=__UpperCamelCase ,				timestep=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				added_cond_kwargs=__UpperCamelCase ,				return_dict=__UpperCamelCase ,				)[0]

					if do_classifier_free_guidance:
						snake_case_		,       snake_case_      		= noise_pred.split(latents.shape[1] ,				dim=1							)
						snake_case_		,       snake_case_      		= noise_pred.chunk(2							)
						snake_case_		,       snake_case_      		= variance_pred.chunk(2							)
						snake_case_      		= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
						snake_case_      		= torch.cat([noise_pred, variance_pred_text] ,				dim=1							)

					if not (
					    hasattr(self.scheduler.config ,				'variance_type'							)
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
						snake_case_		,       snake_case_      		= noise_pred.split(latents.shape[1] ,				dim=1							)

					# compute the previous noisy sample x_t -> x_t-1
					snake_case_      		= self.scheduler.step(
					    __UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				generator=__UpperCamelCase ,				)[0]

				# post-processing
				snake_case_      		= self.movq.decode(__UpperCamelCase ,				force_not_quantize=__UpperCamelCase							)['sample']

				if output_type not in ["pt", "np", "pil"]:
					raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""							)

				if output_type in ["np", "pil"]:
					snake_case_      		= image * 0.5 + 0.5
					snake_case_      		= image.clamp(0 ,				1							)
					snake_case_      		= image.cpu().permute(0 ,				2 ,				3 ,				1							).float().numpy()

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)



import operator as op





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= lambda lowercase__			, lowercase__	: int(x / y	)  # noqa: E731 integer division operation
	snake_case_      		= {
	    '^': 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(lowercase__	))	)

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

			snake_case_      		= stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8	)			, ('pop(' + a + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)

			stack.append(
			    str(opr[x](int(lowercase__	)			, int(lowercase__	)	)	)	)  # 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(lowercase__	)			, sep=' | '			, )

	return int(stack[0]	)


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