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


def A				(__lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Optional[Any]		):
						if not len(__lowerCamelCase		) == rows * cols:
												raise ValueError("""The specified number of rows and columns are not correct."""		)

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

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


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


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

_lowercase    =		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_lowercase    =		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
						_lowercase    =		load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, """unet""", unet)
else:
						_lowercase    =		unet.to(torch.device("""cuda""", args.cuda_id))
_lowercase    =		pipeline.to(unet.device)
_lowercase   ,						_lowercase    =		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()))))
_lowercase    =		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'''



def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:list[int]		):

						# Check if the input is valid
						if not len(__lowerCamelCase		) == len(__lowerCamelCase		) == 3:
												raise ValueError("""Please enter a valid equation."""		)
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("""Both a & b of two equations can't be zero."""		)

						# Extract the coefficients
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa

						# Calculate the determinants of the matrices
						_lowerCAmelCase  						=			aa * ba - aa * ba
						_lowerCAmelCase  						=			ca * ba - ca * ba
						_lowerCAmelCase  						=			aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("""Infinite solutions. (Consistent system)"""		)
												else:
																		raise ValueError("""No solution. (Inconsistent system)"""		)
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowerCAmelCase  						=			determinant_x / determinant
																		_lowerCAmelCase  						=			determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)
