



"""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 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case = "cpu"	,       snake_case = "openai/clip-vit-large-patch14"						)			->							None:
									_UpperCAmelCase							 =		device
									_UpperCAmelCase							 =		CLIPTokenizerFast.from_pretrained(snake_case						)
									_UpperCAmelCase							 =		[0.48145466, 0.4578275, 0.40821073]
									_UpperCAmelCase							 =		[0.26862954, 0.26130258, 0.27577711]
									_UpperCAmelCase							 =		torchvision.transforms.Normalize(self.image_mean	,       self.image_std						)
									_UpperCAmelCase							 =		torchvision.transforms.Resize(224						)
									_UpperCAmelCase							 =		torchvision.transforms.CenterCrop(224						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.resize(snake_case						)
									_UpperCAmelCase							 =		self.center_crop(snake_case						)
									_UpperCAmelCase							 =		self.normalize(snake_case						)
									return images






							def __call__(  self	,       snake_case=None	,       snake_case=None	,       **snake_case						)			->							List[Any]:
									_UpperCAmelCase							 =		self.tokenizer(text=snake_case	,       **snake_case						)
									_UpperCAmelCase							 =		self.preprocess_img(snake_case						)
									_UpperCAmelCase							 =		{key: value.to(self.device						) for (key, value) in encoding.items()}
									return encoding







class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case=10	,       snake_case=0.01	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case=False	,       snake_case=True	,       snake_case="image"	,       snake_case=True	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       )			->							None:
									super().__init__()
									_UpperCAmelCase							 =		None
									_UpperCAmelCase							 =		device if device else get_device()
									if vqgan:
											_UpperCAmelCase							 =		vqgan
									else:
											_UpperCAmelCase							 =		load_vqgan(self.device	,       conf_path=snake_case	,       ckpt_path=snake_case						)
									self.vqgan.eval()
									if clip:
											_UpperCAmelCase							 =		clip
									else:
											_UpperCAmelCase							 =		CLIPModel.from_pretrained('openai/clip-vit-base-patch32'						)
									self.clip.to(self.device						)
									_UpperCAmelCase							 =		ProcessorGradientFlow(device=self.device						)

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






							def        lowerCamelCase_   (  self	,       snake_case=None	,       snake_case=None	,       snake_case=5	,       snake_case=True						)			->							int:
									_UpperCAmelCase							 =		[]
									if output_path is None:
											_UpperCAmelCase							 =		'./animation.gif'
									if input_path is None:
											_UpperCAmelCase							 =		self.save_path
									_UpperCAmelCase							 =		sorted(glob(input_path + '/*'						)						)
									if not len(snake_case						):
											raise ValueError(
											    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
											    ' function?)'						)
									if len(snake_case						) == 1:
											print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'						)
									_UpperCAmelCase							 =		total_duration / len(snake_case						)
									_UpperCAmelCase							 =		[frame_duration] * len(snake_case						)
									if extend_frames:
											_UpperCAmelCase							 =		1.5
											_UpperCAmelCase							 =		3
									for file_name in paths:
											if file_name.endswith('.png'						):
													images.append(imageio.imread(snake_case						)						)
									imageio.mimsave(snake_case	,       snake_case	,       duration=snake_case						)
									print(f'gif saved to {output_path}'						)






							def        lowerCamelCase_   (  self	,       snake_case=None	,       snake_case=None						)			->							Optional[Any]:
									if not (path or img):
											raise ValueError('Input either path or tensor'						)
									if img is not None:
											raise NotImplementedError
									_UpperCAmelCase							 =		preprocess(Image.open(snake_case						)	,       target_image_size=256						).to(self.device						)
									_UpperCAmelCase							 =		preprocess_vqgan(snake_case						)
									_UpperCAmelCase ,					*_UpperCAmelCase							 =		self.vqgan.encode(snake_case						)
									return z






							def        lowerCamelCase_   (  self	,       snake_case						)			->							Any:
									_UpperCAmelCase							 =		self.latent.detach().requires_grad_()
									_UpperCAmelCase							 =		base_latent + transform_vector
									if self.quantize:
											_UpperCAmelCase ,					*_UpperCAmelCase							 =		self.vqgan.quantize(snake_case						)
									else:
											_UpperCAmelCase							 =		trans_latent
									return self.vqgan.decode(snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.clip_preprocessor(text=snake_case	,       images=snake_case	,       return_tensors='pt'	,       padding=snake_case						)
									_UpperCAmelCase							 =		self.clip(**snake_case						)
									_UpperCAmelCase							 =		clip_outputs.logits_per_image
									if weights is not None:
											_UpperCAmelCase							 =		similarity_logits * weights
									return similarity_logits.sum()






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		self._get_clip_similarity(pos_prompts['prompts']	,       snake_case	,       weights=(1 / pos_prompts['weights'])						)
									if neg_prompts:
											_UpperCAmelCase							 =		self._get_clip_similarity(neg_prompts['prompts']	,       snake_case	,       weights=neg_prompts['weights']						)
									else:
											_UpperCAmelCase							 =		torch.tensor([1]	,       device=self.device						)
									_UpperCAmelCase							 =		-torch.log(snake_case						) + torch.log(snake_case						)
									return loss






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		torch.randn_like(self.latent	,       requires_grad=snake_case	,       device=self.device						)
									_UpperCAmelCase							 =		torch.optim.Adam([vector]	,       lr=self.lr						)

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Any:
									wandb.init(reinit=snake_case	,       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:
											_UpperCAmelCase							 =		Image.open(snake_case						)
											_UpperCAmelCase							 =		image.resize((256, 256)						)
											wandb.log('Original Image'	,       wandb.Image(snake_case						)						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							Optional[int]:
									if not prompts:
											return []
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[]
									if isinstance(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		[prompt.strip() for prompt in prompts.split('|'						)]
									for prompt in prompts:
											if isinstance(snake_case	,       (tuple, list)						):
													_UpperCAmelCase							 =		prompt[0]
													_UpperCAmelCase							 =		float(prompt[1]						)
											elif ":" in prompt:
													_UpperCAmelCase ,					_UpperCAmelCase							 =		prompt.split(':'						)
													_UpperCAmelCase							 =		float(snake_case						)
											else:
													_UpperCAmelCase							 =		prompt
													_UpperCAmelCase							 =		1.0
											processed_prompts.append(snake_case						)
											weights.append(snake_case						)
									return {
									    "prompts": processed_prompts,
									    "weights": torch.tensor(snake_case	,       device=self.device						),
									}






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=True	,       snake_case=False	,       snake_case=True	,       snake_case=True	,       snake_case=None	,       )			->							Optional[Any]:
									if image_path:
											_UpperCAmelCase							 =		self._get_latent(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.randn(self.latent_dim	,       device=self.device						)
									if self.log:
											self._init_logging(snake_case	,       snake_case	,       snake_case						)

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

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

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







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''beit'''






							def __init__(  self	,       snake_case=8192	,       snake_case=768	,       snake_case=12	,       snake_case=12	,       snake_case=3072	,       snake_case="gelu"	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=224	,       snake_case=16	,       snake_case=3	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=0.1	,       snake_case=0.1	,       snake_case=True	,       snake_case=[3, 5, 7, 11]	,       snake_case=[1, 2, 3, 6]	,       snake_case=True	,       snake_case=0.4	,       snake_case=256	,       snake_case=1	,       snake_case=False	,       snake_case=255	,       **snake_case	,       )			->							str:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		use_mask_token
									_UpperCAmelCase							 =		use_absolute_position_embeddings
									_UpperCAmelCase							 =		use_relative_position_bias
									_UpperCAmelCase							 =		use_shared_relative_position_bias
									_UpperCAmelCase							 =		layer_scale_init_value
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		use_mean_pooling
									# decode head attributes (semantic segmentation)
									_UpperCAmelCase							 =		out_indices
									_UpperCAmelCase							 =		pool_scales
									# auxiliary head attributes (semantic segmentation)
									_UpperCAmelCase							 =		use_auxiliary_head
									_UpperCAmelCase							 =		auxiliary_loss_weight
									_UpperCAmelCase							 =		auxiliary_channels
									_UpperCAmelCase							 =		auxiliary_num_convs
									_UpperCAmelCase							 =		auxiliary_concat_input
									_UpperCAmelCase							 =		semantic_loss_ignore_index







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


