

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 SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase = "cpu" ,				__UpperCamelCase = "openai/clip-vit-large-patch14"							):

				"""simple docstring"""


				snake_case_      		= device
				snake_case_      		= CLIPTokenizerFast.from_pretrained(__UpperCamelCase							)
				snake_case_      		= [0.4814_5466, 0.457_8275, 0.4082_1073]
				snake_case_      		= [0.2686_2954, 0.2613_0258, 0.2757_7711]
				snake_case_      		= torchvision.transforms.Normalize(self.image_mean ,				self.image_std							)
				snake_case_      		= torchvision.transforms.Resize(2_24							)
				snake_case_      		= torchvision.transforms.CenterCrop(2_24							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.resize(__UpperCamelCase							)
				snake_case_      		= self.center_crop(__UpperCamelCase							)
				snake_case_      		= self.normalize(__UpperCamelCase							)
				return images







			def __call__(    self ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer(text=__UpperCamelCase ,				**__UpperCamelCase							)
				snake_case_      		= self.preprocess_img(__UpperCamelCase							)
				snake_case_      		= {key: value.to(self.device							) for (key, value) in encoding.items()}
				return encoding







class SCREAMING_SNAKE_CASE  (		nn.Module							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase=10 ,				__UpperCamelCase=0.01 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase="image" ,				__UpperCamelCase=True ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				):

				"""simple docstring"""


				super().__init__()
				snake_case_      		= None
				snake_case_      		= device if device else get_device()
				if vqgan:
					snake_case_      		= vqgan
				else:
					snake_case_      		= load_vqgan(self.device ,				conf_path=__UpperCamelCase ,				ckpt_path=__UpperCamelCase							)
				self.vqgan.eval()
				if clip:
					snake_case_      		= clip
				else:
					snake_case_      		= CLIPModel.from_pretrained('openai/clip-vit-base-patch32'							)
				self.clip.to(self.device							)
				snake_case_      		= ProcessorGradientFlow(device=self.device							)

				snake_case_      		= iterations
				snake_case_      		= lr
				snake_case_      		= log
				snake_case_      		= make_grid
				snake_case_      		= return_val
				snake_case_      		= quantize
				snake_case_      		= self.vqgan.decoder.z_shape


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=5 ,				__UpperCamelCase=True							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=None ,				__UpperCamelCase=None							):

				"""simple docstring"""


				if not (path or img):
					raise ValueError('Input either path or tensor'							)
				if img is not None:
					raise NotImplementedError
				snake_case_      		= preprocess(Image.open(__UpperCamelCase							) ,				target_image_size=2_56							).to(self.device							)
				snake_case_      		= preprocess_vqgan(__UpperCamelCase							)
				snake_case_		,       *snake_case_      		= self.vqgan.encode(__UpperCamelCase							)
				return z


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.latent.detach().requires_grad_()
				snake_case_      		= base_latent + transform_vector
				if self.quantize:
					snake_case_		,       *snake_case_      		= self.vqgan.quantize(__UpperCamelCase							)
				else:
					snake_case_      		= trans_latent
				return self.vqgan.decode(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=None							):

				"""simple docstring"""


				snake_case_      		= self.clip_preprocessor(text=__UpperCamelCase ,				images=__UpperCamelCase ,				return_tensors='pt' ,				padding=__UpperCamelCase							)
				snake_case_      		= self.clip(**__UpperCamelCase							)
				snake_case_      		= clip_outputs.logits_per_image
				if weights is not None:
					snake_case_      		= similarity_logits * weights
				return similarity_logits.sum()


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= torch.randn_like(self.latent ,				requires_grad=__UpperCamelCase ,				device=self.device							)
				snake_case_      		= torch.optim.Adam([vector] ,				lr=self.lr							)

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				wandb.init(reinit=__UpperCamelCase ,				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_      		= Image.open(__UpperCamelCase							)
					snake_case_      		= image.resize((2_56, 2_56)							)
					wandb.log('Original Image' ,				wandb.Image(__UpperCamelCase							)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


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







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=None ,				):

				"""simple docstring"""


				if image_path:
					snake_case_      		= self._get_latent(__UpperCamelCase							)
				else:
					snake_case_      		= torch.randn(self.latent_dim ,				device=self.device							)
				if self.log:
					self._init_logging(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

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

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

				snake_case_      		= loop_post_process(__UpperCamelCase							)
				for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)							):
					if show_intermediate:
						show_pil(__UpperCamelCase							)
					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(__UpperCamelCase							)}							)
				if show_final:
					show_pil(__UpperCamelCase							)
				if save_final:
					transformed_img.save(os.path.join(self.save_path ,				f"""iter_{iter:03d}_final.png"""							)							)



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(lowercase__	)

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()
