




'''simple docstring'''

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


def __UpperCAmelCase	(   _UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   str						)      ->      List[str]:



			if not len(_UpperCamelCase						) == rows * cols:
						raise ValueError("The specified number of rows and columns are not correct."						)

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

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


def __UpperCAmelCase	(   _UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   List[str]="robotic cat with wings"		,				_UpperCAmelCase		:   int=7.5		,				_UpperCAmelCase		:   Dict=50		,				_UpperCAmelCase		:   Dict=1		,				_UpperCAmelCase		:   Tuple=42		,				)      ->      List[Any]:
			__snake_case			=		torch.Generator(pipeline.device						).manual_seed(_UpperCamelCase						)
			__snake_case			=		pipeline(
			    _UpperCamelCase		,				guidance_scale=_UpperCamelCase		,				num_inference_steps=_UpperCamelCase		,				generator=_UpperCamelCase		,				num_images_per_prompt=_UpperCamelCase		,				).images
			__snake_case			=		int(math.sqrt(_UpperCamelCase						)						)
			__snake_case			=		image_grid(_UpperCamelCase		,				rows=_rows		,				cols=num_images_per_prompt // _rows						)
			return grid, images


a       :						Union[str, Any]								=		parse_args()
# Load models and create wrapper for stable diffusion
a       :						str								=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
a       :						str								=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
a       :						str								=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
a       :						Any								=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

a       :						Tuple								=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
a       :						List[Any]								=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
						a       :						str								=		load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, '''unet''', unet)
else:
						a       :						Union[str, Any]								=		unet.to(torch.device('''cuda''', args.cuda_id))
a       :						List[str]								=		pipeline.to(unet.device)
a       :						int								=		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       :						Optional[Any]								=		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)))







"""simple docstring"""




from __future__ import annotations

import typing
from collections import Counter




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      typing.Counter[int]   			=  Counter()
  for base in range(1			,					max_perimeter + 1    ):
    for perpendicular in range(_UpperCamelCase			,					max_perimeter + 1    ):
      _lowercase:      str   			=  (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(_UpperCamelCase    ):
        _lowercase:      str   			=  int(base + perpendicular + hypotenuse    )
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets




def 			_lowerCAmelCase		(		_UpperCamelCase = 1_000    ):






  """simple docstring"""



  _lowercase:      int   			=  pythagorean_triple(_UpperCamelCase    )
  return triplets.most_common(1    )[0][0]


if __name__ == "__main__":
  print(f"""Perimeter {solution()} has maximum solutions""")
