





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 					lowerCamelCase_						(    )		->						List[Any]:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


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

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

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase="robotic cat with wings"			,    _UpperCamelCase=7.5			,    _UpperCamelCase=50			,    _UpperCamelCase=1			,    _UpperCamelCase=42			,    )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  torch.Generator(pipeline.device			).manual_seed(_SCREAMING_SNAKE_CASE			)
				snake_case_						:   Tuple           =  pipeline(
				    _SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    ).images
				snake_case_						:   Dict           =  int(math.sqrt(_SCREAMING_SNAKE_CASE			)			)
				snake_case_						:   Dict           =  image_grid(_SCREAMING_SNAKE_CASE			,    rows=_rows			,    cols=num_images_per_prompt // _rows			)
				return grid, images


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

lowerCAmelCase_    =    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase_    =    lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
	lowerCAmelCase_    =    load(args.pretrained_model_name_or_path, model=unet)
	unet.eval()
	setattr(pipeline, '''unet''', unet)
else:
	lowerCAmelCase_    =    unet.to(torch.device('''cuda''', args.cuda_id))
lowerCAmelCase_    =    pipeline.to(unet.device)
lowerCAmelCase_  , lowerCAmelCase_    =    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()))))
lowerCAmelCase_    =    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 numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  xgboost(_UpperCamelCase			,    _UpperCamelCase			)

				# Display the confusion matrix of the classifier with both training and test sets
				ConfusionMatrixDisplay.from_estimator(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    display_labels=_UpperCamelCase			,    cmap='''Blues'''			,    normalize='''true'''			,    )
				plt.title('''Normalized Confusion Matrix - IRIS Dataset'''			)
				plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()
