

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser()
	parser.add_argument(
	    '-m'			, '--pretrained_model_name_or_path'			, type=lowercase__			, default=lowercase__			, required=lowercase__			, help='Path to pretrained model or model identifier from huggingface.co/models.'			, )
	parser.add_argument(
	    '-c'			, '--caption'			, type=lowercase__			, default='robotic cat with wings'			, help='Text used to generate images.'			, )
	parser.add_argument(
	    '-n'			, '--images_num'			, type=lowercase__			, default=4			, help='How much images to generate.'			, )
	parser.add_argument(
	    '-s'			, '--seed'			, type=lowercase__			, default=42			, help='Seed for random process.'			, )
	parser.add_argument(
	    '-ci'			, '--cuda_id'			, type=lowercase__			, default=0			, help='cuda_id.'			, )
	snake_case_      		= parser.parse_args()
	return args





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	if not len(lowercase__	) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'	)

	snake_case_		,       snake_case_      		= imgs[0].size
	snake_case_      		= Image.new('RGB'			, size=(cols * w, rows * h)	)
	snake_case_		,       snake_case_      		= grid.size

	for i, img in enumerate(lowercase__	):
		grid.paste(lowercase__			, box=(i % cols * w, i // cols * h)	)
	return grid





def a(lowercase__			, lowercase__="robotic cat with wings"			, lowercase__=7.5			, lowercase__=50			, lowercase__=1			, lowercase__=42			, ):





	'''simple docstring'''
	snake_case_      		= torch.Generator(pipeline.device	).manual_seed(lowercase__	)
	snake_case_      		= pipeline(
	    lowercase__			, guidance_scale=lowercase__			, num_inference_steps=lowercase__			, generator=lowercase__			, num_images_per_prompt=lowercase__			, ).images
	snake_case_      		= int(math.sqrt(lowercase__	)	)
	snake_case_      		= image_grid(lowercase__			, rows=_rows			, cols=num_images_per_prompt // _rows	)
	return grid, images


A											=     parse_args()
# Load models and create wrapper for stable diffusion
A											=     CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
A											=     CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
A											=     AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
A											=     UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

A											=     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A											=     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
		A											=     load(args.pretrained_model_name_or_path, model=unet)
		unet.eval()
		setattr(pipeline, 'unet', unet)
else:
		A											=     unet.to(torch.device('cuda', args.cuda_id))
A											=     pipeline.to(unet.device)
A							,  A											=     generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
A											=     os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
		image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))



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))
