





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ = "cpu" ,							__magic_name__ = "openai/clip-vit-large-patch14"   )					->       None:


							'''simple docstring'''



							snake_case_						:   Any           =  device
							snake_case_						:   Dict           =  CLIPTokenizerFast.from_pretrained(__magic_name__   )
							snake_case_						:   Any           =  [0.48_145_466, 0.4_578_275, 0.40_821_073]
							snake_case_						:   Any           =  [0.26_862_954, 0.26_130_258, 0.27_577_711]
							snake_case_						:   str           =  torchvision.transforms.Normalize(self.image_mean ,							self.image_std   )
							snake_case_						:   Dict           =  torchvision.transforms.Resize(224   )
							snake_case_						:   List[Any]           =  torchvision.transforms.CenterCrop(224   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.resize(__magic_name__   )
							snake_case_						:   Tuple           =  self.center_crop(__magic_name__   )
							snake_case_						:   Any           =  self.normalize(__magic_name__   )
							return images






			def __call__(self ,							__magic_name__=None ,							__magic_name__=None ,							**__magic_name__   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer(text=__magic_name__ ,							**__magic_name__   )
							snake_case_						:   Dict           =  self.preprocess_img(__magic_name__   )
							snake_case_						:   str           =  {key: value.to(self.device   ) for (key, value) in encoding.items()}
							return encoding




class  __lowerCAmelCase	(      nn.Module					):






			def __init__(self ,							__magic_name__=10 ,							__magic_name__=0.01 ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=False ,							__magic_name__=True ,							__magic_name__="image" ,							__magic_name__=True ,							__magic_name__=False ,							__magic_name__=False ,							__magic_name__=False ,							)					->       None:


							'''simple docstring'''



							super().__init__()
							snake_case_						:   Dict           =  None
							snake_case_						:   List[Any]           =  device if device else get_device()
							if vqgan:
											snake_case_						:   List[Any]           =  vqgan
							else:
											snake_case_						:   Dict           =  load_vqgan(self.device ,							conf_path=__magic_name__ ,							ckpt_path=__magic_name__   )
							self.vqgan.eval()
							if clip:
											snake_case_						:   int           =  clip
							else:
											snake_case_						:   List[str]           =  CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''   )
							self.clip.to(self.device   )
							snake_case_						:   str           =  ProcessorGradientFlow(device=self.device   )

							snake_case_						:   Optional[Any]           =  iterations
							snake_case_						:   str           =  lr
							snake_case_						:   Union[str, Any]           =  log
							snake_case_						:   Tuple           =  make_grid
							snake_case_						:   Optional[Any]           =  return_val
							snake_case_						:   Union[str, Any]           =  quantize
							snake_case_						:   Optional[Any]           =  self.vqgan.decoder.z_shape






			def       lowerCamelCase  (self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=5 ,							__magic_name__=True   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Any           =  []
							if output_path is None:
											snake_case_						:   List[Any]           =  '''./animation.gif'''
							if input_path is None:
											snake_case_						:   Dict           =  self.save_path
							snake_case_						:   Tuple           =  sorted(glob(input_path + '''/*'''   )   )
							if not len(__magic_name__   ):
											raise ValueError(
											    '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
											    ''' function?)'''   )
							if len(__magic_name__   ) == 1:
											print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''   )
							snake_case_						:   Union[str, Any]           =  total_duration / len(__magic_name__   )
							snake_case_						:   str           =  [frame_duration] * len(__magic_name__   )
							if extend_frames:
											snake_case_						:   int           =  1.5
											snake_case_						:   Dict           =  3
							for file_name in paths:
											if file_name.endswith('''.png'''   ):
															images.append(imageio.imread(__magic_name__   )   )
							imageio.mimsave(__magic_name__ ,							__magic_name__ ,							duration=__magic_name__   )
							print(F'''gif saved to {output_path}'''   )






			def       lowerCamelCase  (self ,							__magic_name__=None ,							__magic_name__=None   )					->       str:


							'''simple docstring'''



							if not (path or img):
											raise ValueError('''Input either path or tensor'''   )
							if img is not None:
											raise NotImplementedError
							snake_case_						:   Any           =  preprocess(Image.open(__magic_name__   ) ,							target_image_size=256   ).to(self.device   )
							snake_case_						:   Union[str, Any]           =  preprocess_vqgan(__magic_name__   )
							snake_case_      ,							*snake_case_						:   Optional[Any]           =  self.vqgan.encode(__magic_name__   )
							return z






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  self.latent.detach().requires_grad_()
							snake_case_						:   Optional[int]           =  base_latent + transform_vector
							if self.quantize:
											snake_case_      ,							*snake_case_						:   Dict           =  self.vqgan.quantize(__magic_name__   )
							else:
											snake_case_						:   int           =  trans_latent
							return self.vqgan.decode(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.clip_preprocessor(text=__magic_name__ ,							images=__magic_name__ ,							return_tensors='''pt''' ,							padding=__magic_name__   )
							snake_case_						:   Any           =  self.clip(**__magic_name__   )
							snake_case_						:   List[str]           =  clip_outputs.logits_per_image
							if weights is not None:
											snake_case_						:   Dict           =  similarity_logits * weights
							return similarity_logits.sum()






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   int           =  self._get_clip_similarity(pos_prompts['''prompts'''] ,							__magic_name__ ,							weights=(1 / pos_prompts['''weights'''])   )
							if neg_prompts:
											snake_case_						:   str           =  self._get_clip_similarity(neg_prompts['''prompts'''] ,							__magic_name__ ,							weights=neg_prompts['''weights''']   )
							else:
											snake_case_						:   Optional[Any]           =  torch.tensor([1] ,							device=self.device   )
							snake_case_						:   Optional[int]           =  -torch.log(__magic_name__   ) + torch.log(__magic_name__   )
							return loss






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   str           =  torch.randn_like(self.latent ,							requires_grad=__magic_name__ ,							device=self.device   )
							snake_case_						:   Tuple           =  torch.optim.Adam([vector] ,							lr=self.lr   )

							for i in range(self.iterations   ):
											optim.zero_grad()
											snake_case_						:   List[Any]           =  self._add_vector(__magic_name__   )
											snake_case_						:   int           =  loop_post_process(__magic_name__   )
											snake_case_						:   Union[str, Any]           =  self._get_CLIP_loss(__magic_name__ ,							__magic_name__ ,							__magic_name__   )
											print('''CLIP loss''' ,							__magic_name__   )
											if self.log:
															wandb.log({'''CLIP Loss''': clip_loss}   )
											clip_loss.backward(retain_graph=__magic_name__   )
											optim.step()
											if self.return_val == "image":
															yield custom_to_pil(transformed_img[0]   )
											else:
															yield vector






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


							'''simple docstring'''



							wandb.init(reinit=__magic_name__ ,							project='''face-editor'''   )
							wandb.config.update({'''Positive Prompts''': positive_prompts}   )
							wandb.config.update({'''Negative Prompts''': negative_prompts}   )
							wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}   )
							if image_path:
											snake_case_						:   Dict           =  Image.open(__magic_name__   )
											snake_case_						:   int           =  image.resize((256, 256)   )
											wandb.log('''Original Image''' ,							wandb.Image(__magic_name__   )   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							if not prompts:
											return []
							snake_case_						:   str           =  []
							snake_case_						:   str           =  []
							if isinstance(__magic_name__ ,							__magic_name__   ):
											snake_case_						:   Union[str, Any]           =  [prompt.strip() for prompt in prompts.split('''|'''   )]
							for prompt in prompts:
											if isinstance(__magic_name__ ,							(tuple, list)   ):
															snake_case_						:   Tuple           =  prompt[0]
															snake_case_						:   Any           =  float(prompt[1]   )
											elif ":" in prompt:
															snake_case_      ,							snake_case_						:   Union[str, Any]           =  prompt.split(''':'''   )
															snake_case_						:   int           =  float(__magic_name__   )
											else:
															snake_case_						:   str           =  prompt
															snake_case_						:   Dict           =  1.0
											processed_prompts.append(__magic_name__   )
											weights.append(__magic_name__   )
							return {
							    "prompts": processed_prompts,
							    "weights": torch.tensor(__magic_name__ ,							device=self.device   ),
							}






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=True ,							__magic_name__=False ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=None ,							)					->       Any:


							'''simple docstring'''



							if image_path:
											snake_case_						:   Optional[int]           =  self._get_latent(__magic_name__   )
							else:
											snake_case_						:   List[Any]           =  torch.randn(self.latent_dim ,							device=self.device   )
							if self.log:
											self._init_logging(__magic_name__ ,							__magic_name__ ,							__magic_name__   )

							assert pos_prompts, "You must provide at least one positive prompt."
							snake_case_						:   Tuple           =  self.process_prompts(__magic_name__   )
							snake_case_						:   Dict           =  self.process_prompts(__magic_name__   )
							if save_final and save_path is None:
											snake_case_						:   Any           =  os.path.join('''./outputs/''' ,							'''_'''.join(pos_prompts['''prompts''']   )   )
							if not os.path.exists(__magic_name__   ):
											os.makedirs(__magic_name__   )
							else:
											snake_case_						:   Optional[int]           =  save_path + '''_''' + get_timestamp()
											os.makedirs(__magic_name__   )
							snake_case_						:   str           =  save_path

							snake_case_						:   Tuple           =  self.vqgan.decode(self.latent   )[0]
							if show_intermediate:
											print('''Original Image'''   )
											show_pil(custom_to_pil(__magic_name__   )   )

							snake_case_						:   Tuple           =  loop_post_process(__magic_name__   )
							for iter, transformed_img in enumerate(self._optimize_CLIP(__magic_name__ ,							__magic_name__ ,							__magic_name__   )   ):
											if show_intermediate:
															show_pil(__magic_name__   )
											if save_intermediate:
															transformed_img.save(os.path.join(self.save_path ,							F'''iter_{iter:03d}.png'''   )   )
											if self.log:
															wandb.log({'''Image''': wandb.Image(__magic_name__   )}   )
							if show_final:
											show_pil(__magic_name__   )
							if save_final:
											transformed_img.save(os.path.join(self.save_path ,							F'''iter_{iter:03d}_final.png'''   )   )







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()
