


"""simple docstring"""






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_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__ = "cpu"       ,		UpperCAmelCase__ = "openai/clip-vit-large-patch14"     ):
								SCREAMING_SNAKE_CASE__     							=				device
								SCREAMING_SNAKE_CASE__     							=				CLIPTokenizerFast.from_pretrained(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				[0.48_145_466, 0.4_578_275, 0.40_821_073]
								SCREAMING_SNAKE_CASE__     							=				[0.26_862_954, 0.26_130_258, 0.27_577_711]
								SCREAMING_SNAKE_CASE__     							=				torchvision.transforms.Normalize(self.image_mean       ,		self.image_std     )
								SCREAMING_SNAKE_CASE__     							=				torchvision.transforms.Resize(224     )
								SCREAMING_SNAKE_CASE__     							=				torchvision.transforms.CenterCrop(224     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.resize(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.center_crop(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.normalize(UpperCAmelCase__     )
								return images






				def __call__(						self       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		**UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer(text=UpperCAmelCase__       ,		**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.preprocess_img(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				{key: value.to(self.device     ) for (key, value) in encoding.items()}
								return encoding


class 			lowerCamelCase_		(			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       ,		):
								super().__init__()
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				device if device else get_device()
								if vqgan:
												SCREAMING_SNAKE_CASE__     							=				vqgan
								else:
												SCREAMING_SNAKE_CASE__     							=				load_vqgan(self.device       ,		conf_path=UpperCAmelCase__       ,		ckpt_path=UpperCAmelCase__     )
								self.vqgan.eval()
								if clip:
												SCREAMING_SNAKE_CASE__     							=				clip
								else:
												SCREAMING_SNAKE_CASE__     							=				CLIPModel.from_pretrained("openai/clip-vit-base-patch32"     )
								self.clip.to(self.device     )
								SCREAMING_SNAKE_CASE__     							=				ProcessorGradientFlow(device=self.device     )

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



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=5       ,		UpperCAmelCase__=True     ):
								SCREAMING_SNAKE_CASE__     							=				[]
								if output_path is None:
												SCREAMING_SNAKE_CASE__     							=				"./animation.gif"
								if input_path is None:
												SCREAMING_SNAKE_CASE__     							=				self.save_path
								SCREAMING_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?)"     )
								SCREAMING_SNAKE_CASE__     							=				total_duration / len(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				[frame_duration] * len(UpperCAmelCase__     )
								if extend_frames:
												SCREAMING_SNAKE_CASE__     							=				1.5
												SCREAMING_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     ):
								if not (path or img):
												raise ValueError("Input either path or tensor"     )
								if img is not None:
												raise NotImplementedError
								SCREAMING_SNAKE_CASE__     							=				preprocess(Image.open(UpperCAmelCase__     )       ,		target_image_size=256     ).to(self.device     )
								SCREAMING_SNAKE_CASE__     							=				preprocess_vqgan(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__     							=				self.vqgan.encode(UpperCAmelCase__     )
								return z



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.latent.detach().requires_grad_()
								SCREAMING_SNAKE_CASE__     							=				base_latent + transform_vector
								if self.quantize:
												SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__     							=				self.vqgan.quantize(UpperCAmelCase__     )
								else:
												SCREAMING_SNAKE_CASE__     							=				trans_latent
								return self.vqgan.decode(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								SCREAMING_SNAKE_CASE__     							=				self.clip_preprocessor(text=UpperCAmelCase__       ,		images=UpperCAmelCase__       ,		return_tensors="pt"       ,		padding=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.clip(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				clip_outputs.logits_per_image
								if weights is not None:
												SCREAMING_SNAKE_CASE__     							=				similarity_logits * weights
								return similarity_logits.sum()



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self._get_clip_similarity(pos_prompts["prompts"]       ,		UpperCAmelCase__       ,		weights=(1 / pos_prompts["weights"])     )
								if neg_prompts:
												SCREAMING_SNAKE_CASE__     							=				self._get_clip_similarity(neg_prompts["prompts"]       ,		UpperCAmelCase__       ,		weights=neg_prompts["weights"]     )
								else:
												SCREAMING_SNAKE_CASE__     							=				torch.tensor([1]       ,		device=self.device     )
								SCREAMING_SNAKE_CASE__     							=				-torch.log(UpperCAmelCase__     ) + torch.log(UpperCAmelCase__     )
								return loss



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				torch.randn_like(self.latent       ,		requires_grad=UpperCAmelCase__       ,		device=self.device     )
								SCREAMING_SNAKE_CASE__     							=				torch.optim.Adam([vector]       ,		lr=self.lr     )

								for i in range(self.iterations     ):
												optim.zero_grad()
												SCREAMING_SNAKE_CASE__     							=				self._add_vector(UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				loop_post_process(UpperCAmelCase__     )
												SCREAMING_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__     ):
								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:
												SCREAMING_SNAKE_CASE__     							=				Image.open(UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				image.resize((256, 256)     )
												wandb.log("Original Image"       ,		wandb.Image(UpperCAmelCase__     )     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								if not prompts:
												return []
								SCREAMING_SNAKE_CASE__     							=				[]
								SCREAMING_SNAKE_CASE__     							=				[]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				[prompt.strip() for prompt in prompts.split("|"     )]
								for prompt in prompts:
												if isinstance(UpperCAmelCase__       ,		(tuple, list)     ):
																SCREAMING_SNAKE_CASE__     							=				prompt[0]
																SCREAMING_SNAKE_CASE__     							=				float(prompt[1]     )
												elif ":" in prompt:
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				prompt.split(":"     )
																SCREAMING_SNAKE_CASE__     							=				float(UpperCAmelCase__     )
												else:
																SCREAMING_SNAKE_CASE__     							=				prompt
																SCREAMING_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       ,		):
								if image_path:
												SCREAMING_SNAKE_CASE__     							=				self._get_latent(UpperCAmelCase__     )
								else:
												SCREAMING_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."
								SCREAMING_SNAKE_CASE__     							=				self.process_prompts(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.process_prompts(UpperCAmelCase__     )
								if save_final and save_path is None:
												SCREAMING_SNAKE_CASE__     							=				os.path.join("./outputs/"       ,		"_".join(pos_prompts["prompts"]     )     )
								if not os.path.exists(UpperCAmelCase__     ):
												os.makedirs(UpperCAmelCase__     )
								else:
												SCREAMING_SNAKE_CASE__     							=				save_path + "_" + get_timestamp()
												os.makedirs(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				save_path

								SCREAMING_SNAKE_CASE__     							=				self.vqgan.decode(self.latent     )[0]
								if show_intermediate:
												print("Original Image"     )
												show_pil(custom_to_pil(UpperCAmelCase__     )     )

								SCREAMING_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'''     )     )




"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase     =  {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase     =  [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	_lowerCamelCase     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
