
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       __A:

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     str = "cpu"   ,					__UpperCamelCase					:     str = "openai/clip-vit-large-patch14"   ):
												lowerCamelCase_							  =					device
												lowerCamelCase_							  =					CLIPTokenizerFast.from_pretrained(__UpperCamelCase   )
												lowerCamelCase_							  =					[0.48145466, 0.4578275, 0.40821073]
												lowerCamelCase_							  =					[0.26862954, 0.26130258, 0.27577711]
												lowerCamelCase_							  =					torchvision.transforms.Normalize(self.image_mean   ,					self.image_std   )
												lowerCamelCase_							  =					torchvision.transforms.Resize(2_2_4   )
												lowerCamelCase_							  =					torchvision.transforms.CenterCrop(2_2_4   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					self.resize(__UpperCamelCase   )
												lowerCamelCase_							  =					self.center_crop(__UpperCamelCase   )
												lowerCamelCase_							  =					self.normalize(__UpperCamelCase   )
												return images



					def __call__(	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]=None   ,					__UpperCamelCase					:     List[Any]=None   ,					**__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.tokenizer(text=__UpperCamelCase   ,					**__UpperCamelCase   )
												lowerCamelCase_							  =					self.preprocess_img(__UpperCamelCase   )
												lowerCamelCase_							  =					{key: value.to(self.device   ) for (key, value) in encoding.items()}
												return encoding





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     Dict=1_0   ,					__UpperCamelCase					:     Optional[int]=0.01   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Union[str, Any]=None   ,					__UpperCamelCase					:     Any=None   ,					__UpperCamelCase					:     List[Any]=None   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     Tuple=None   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     Union[str, Any]="image"   ,					__UpperCamelCase					:     List[Any]=True   ,					__UpperCamelCase					:     Optional[Any]=False   ,					__UpperCamelCase					:     Optional[Any]=False   ,					__UpperCamelCase					:     Optional[Any]=False   ,					):
												super().__init__()
												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					device if device else get_device()
												if vqgan:
																			lowerCamelCase_							  =					vqgan
												else:
																			lowerCamelCase_							  =					load_vqgan(self.device   ,					conf_path=__UpperCamelCase   ,					ckpt_path=__UpperCamelCase   )
												self.vqgan.eval()
												if clip:
																			lowerCamelCase_							  =					clip
												else:
																			lowerCamelCase_							  =					CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""   )
												self.clip.to(self.device   )
												lowerCamelCase_							  =					ProcessorGradientFlow(device=self.device   )

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

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     str=5   ,					__UpperCamelCase					:     int=True   ):
												lowerCamelCase_							  =					[]
												if output_path is None:
																			lowerCamelCase_							  =					"""./animation.gif"""
												if input_path is None:
																			lowerCamelCase_							  =					self.save_path
												lowerCamelCase_							  =					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?)"""   )
												lowerCamelCase_							  =					total_duration / len(__UpperCamelCase   )
												lowerCamelCase_							  =					[frame_duration] * len(__UpperCamelCase   )
												if extend_frames:
																			lowerCamelCase_							  =					1.5
																			lowerCamelCase_							  =					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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]=None   ,					__UpperCamelCase					:     Dict=None   ):
												if not (path or img):
																			raise ValueError("""Input either path or tensor"""   )
												if img is not None:
																			raise NotImplementedError
												lowerCamelCase_							  =					preprocess(Image.open(__UpperCamelCase   )   ,					target_image_size=2_5_6   ).to(self.device   )
												lowerCamelCase_							  =					preprocess_vqgan(__UpperCamelCase   )
												lowerCamelCase_  ,   *lowerCamelCase_							  =					self.vqgan.encode(__UpperCamelCase   )
												return z

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					self.latent.detach().requires_grad_()
												lowerCamelCase_							  =					base_latent + transform_vector
												if self.quantize:
																			lowerCamelCase_  ,   *lowerCamelCase_							  =					self.vqgan.quantize(__UpperCamelCase   )
												else:
																			lowerCamelCase_							  =					trans_latent
												return self.vqgan.decode(__UpperCamelCase   )

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.clip_preprocessor(text=__UpperCamelCase   ,					images=__UpperCamelCase   ,					return_tensors="""pt"""   ,					padding=__UpperCamelCase   )
												lowerCamelCase_							  =					self.clip(**__UpperCamelCase   )
												lowerCamelCase_							  =					clip_outputs.logits_per_image
												if weights is not None:
																			lowerCamelCase_							  =					similarity_logits * weights
												return similarity_logits.sum()

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self._get_clip_similarity(pos_prompts["""prompts"""]   ,					__UpperCamelCase   ,					weights=(1 / pos_prompts["""weights"""])   )
												if neg_prompts:
																			lowerCamelCase_							  =					self._get_clip_similarity(neg_prompts["""prompts"""]   ,					__UpperCamelCase   ,					weights=neg_prompts["""weights"""]   )
												else:
																			lowerCamelCase_							  =					torch.tensor([1]   ,					device=self.device   )
												lowerCamelCase_							  =					-torch.log(__UpperCamelCase   ) + torch.log(__UpperCamelCase   )
												return loss

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[int]   ):
												lowerCamelCase_							  =					torch.randn_like(self.latent   ,					requires_grad=__UpperCamelCase   ,					device=self.device   )
												lowerCamelCase_							  =					torch.optim.Adam([vector]   ,					lr=self.lr   )

												for i in range(self.iterations   ):
																			optim.zero_grad()
																			lowerCamelCase_							  =					self._add_vector(__UpperCamelCase   )
																			lowerCamelCase_							  =					loop_post_process(__UpperCamelCase   )
																			lowerCamelCase_							  =					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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Any   ):
												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:
																			lowerCamelCase_							  =					Image.open(__UpperCamelCase   )
																			lowerCamelCase_							  =					image.resize((2_5_6, 2_5_6)   )
																			wandb.log("""Original Image"""   ,					wandb.Image(__UpperCamelCase   )   )

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



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]=None   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     List[Any]=True   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     Optional[int]=True   ,					__UpperCamelCase					:     List[Any]=True   ,					__UpperCamelCase					:     Optional[int]=None   ,					):
												if image_path:
																			lowerCamelCase_							  =					self._get_latent(__UpperCamelCase   )
												else:
																			lowerCamelCase_							  =					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."
												lowerCamelCase_							  =					self.process_prompts(__UpperCamelCase   )
												lowerCamelCase_							  =					self.process_prompts(__UpperCamelCase   )
												if save_final and save_path is None:
																			lowerCamelCase_							  =					os.path.join("""./outputs/"""   ,					"""_""".join(pos_prompts["""prompts"""]   )   )
												if not os.path.exists(__UpperCamelCase   ):
																			os.makedirs(__UpperCamelCase   )
												else:
																			lowerCamelCase_							  =					save_path + """_""" + get_timestamp()
																			os.makedirs(__UpperCamelCase   )
												lowerCamelCase_							  =					save_path

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

												lowerCamelCase_							  =					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'''   )   )







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					do_resize
												lowerCamelCase_							  =					size
												# Default value set here for backwards compatibility where the value in config is None
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												lowerCamelCase_							  =					resample
												lowerCamelCase_							  =					do_rescale
												lowerCamelCase_							  =					rescale_factor
												lowerCamelCase_							  =					do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCamelCase_							  =					image_std if image_std is not None else IMAGENET_STANDARD_STD

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												if "shortest_edge" not in size:
																			raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
												lowerCamelCase_							  =					size["""shortest_edge"""]

												if shortest_edge < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[int, float]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     int   ,					):
												return rescale(__UpperCamelCase   ,					scale=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     List[str]   ,					):
												return normalize(__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     ChannelDimension = ChannelDimension.FIRST   ,					**__UpperCamelCase					:     List[str]   ,					):
												lowerCamelCase_							  =					do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else self.crop_pct
												lowerCamelCase_							  =					resample if resample is not None else self.resample
												lowerCamelCase_							  =					do_rescale if do_rescale is not None else self.do_rescale
												lowerCamelCase_							  =					rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCamelCase_							  =					do_normalize if do_normalize is not None else self.do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else self.image_mean
												lowerCamelCase_							  =					image_std if image_std is not None else self.image_std

												lowerCamelCase_							  =					size if size is not None else self.size
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					make_list_of_images(__UpperCamelCase   )

												if not valid_images(__UpperCamelCase   ):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""   )

												if do_resize and size is None or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""   )

												if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""   )

												if do_rescale and rescale_factor is None:
																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

												if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""   )

												# All transformations expect numpy arrays.
												lowerCamelCase_							  =					[to_numpy_array(__UpperCamelCase   ) for image in images]

												if do_resize:
																			lowerCamelCase_							  =					[self.resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					crop_pct=__UpperCamelCase   ,					resample=__UpperCamelCase   ) for image in images]

												if do_rescale:
																			lowerCamelCase_							  =					[self.rescale(image=__UpperCamelCase   ,					scale=__UpperCamelCase   ) for image in images]

												if do_normalize:
																			lowerCamelCase_							  =					[self.normalize(image=__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					[to_channel_dimension_format(__UpperCamelCase   ,					__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





