



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   UpperCAmelCase_    :







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

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    device
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizerFast.from_pretrained(__a)
														_lowerCAmelCase     :							Any    =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
														_lowerCAmelCase     :							Union[str, Any]    =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
														_lowerCAmelCase     :							Tuple    =    torchvision.transforms.Normalize(self.image_mean,			self.image_std)
														_lowerCAmelCase     :							Optional[int]    =    torchvision.transforms.Resize(224)
														_lowerCAmelCase     :							Dict    =    torchvision.transforms.CenterCrop(224)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.resize(__a)
														_lowerCAmelCase     :							List[str]    =    self.center_crop(__a)
														_lowerCAmelCase     :							Optional[Any]    =    self.normalize(__a)
														return images







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

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.tokenizer(text=__a,			**__a)
														_lowerCAmelCase     :							List[str]    =    self.preprocess_img(__a)
														_lowerCAmelCase     :							Tuple    =    {key: value.to(self.device) for (key, value) in encoding.items()}
														return encoding



class   UpperCAmelCase_    (       nn.Module):







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

														'''simple docstring'''

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

														_lowerCAmelCase     :							Any    =    iterations
														_lowerCAmelCase     :							List[Any]    =    lr
														_lowerCAmelCase     :							Tuple    =    log
														_lowerCAmelCase     :							List[str]    =    make_grid
														_lowerCAmelCase     :							int    =    return_val
														_lowerCAmelCase     :							Dict    =    quantize
														_lowerCAmelCase     :							Any    =    self.vqgan.decoder.z_shape







							def    snake_case__      (   self,			__a=None,			__a=None,			__a=5,			__a=True):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a=None,			__a=None):

														'''simple docstring'''

														if not (path or img):
																					raise ValueError("Input either path or tensor")
														if img is not None:
																					raise NotImplementedError
														_lowerCAmelCase     :							Dict    =    preprocess(Image.open(__a),			target_image_size=256).to(self.device)
														_lowerCAmelCase     :							Dict    =    preprocess_vqgan(__a)
														_lowerCAmelCase   ,    *_lowerCAmelCase     :							str    =    self.vqgan.encode(__a)
														return z







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.clip_preprocessor(text=__a,			images=__a,			return_tensors="pt",			padding=__a)
														_lowerCAmelCase     :							Optional[int]    =    self.clip(**__a)
														_lowerCAmelCase     :							Any    =    clip_outputs.logits_per_image
														if weights is not None:
																					_lowerCAmelCase     :							Tuple    =    similarity_logits * weights
														return similarity_logits.sum()







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.randn_like(self.latent,			requires_grad=__a,			device=self.device)
														_lowerCAmelCase     :							Optional[int]    =    torch.optim.Adam([vector],			lr=self.lr)

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







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														wandb.init(reinit=__a,			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     :							str    =    Image.open(__a)
																					_lowerCAmelCase     :							int    =    image.resize((256, 256))
																					wandb.log("Original Image",			wandb.Image(__a))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a=None,			__a=None,			__a=True,			__a=False,			__a=True,			__a=True,			__a=None,			):

														'''simple docstring'''

														if image_path:
																					_lowerCAmelCase     :							List[Any]    =    self._get_latent(__a)
														else:
																					_lowerCAmelCase     :							Any    =    torch.randn(self.latent_dim,			device=self.device)
														if self.log:
																					self._init_logging(__a,			__a,			__a)

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

														_lowerCAmelCase     :							List[Any]    =    self.vqgan.decode(self.latent)[0]
														if show_intermediate:
																					print("Original Image")
																					show_pil(custom_to_pil(__a))

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





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							def constraint_to_multiple_of(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0				, _lowerCamelCase=None  ):
														_lowerCAmelCase     :							Tuple    =    round(val / multiple  ) * multiple

														if max_val is not None and x > max_val:
																					_lowerCAmelCase     :							Optional[int]    =    math.floor(val / multiple  ) * multiple

														if x < min_val:
																					_lowerCAmelCase     :							List[str]    =    math.ceil(val / multiple  ) * multiple

														return x

							_lowerCAmelCase     :							Union[str, Any]    =    (output_size, output_size) if isinstance(_lowerCamelCase				, _lowerCamelCase  ) else output_size

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    get_image_size(_lowerCamelCase  )
							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    output_size

							# determine new height and width
							_lowerCAmelCase     :							List[Any]    =    output_height / input_height
							_lowerCAmelCase     :							Any    =    output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width  ) < abs(1 - scale_height  ):
																					# fit width
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_width
														else:
																					# fit height
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_height

							_lowerCAmelCase     :							List[str]    =    constraint_to_multiple_of(scale_height * input_height				, multiple=_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    constraint_to_multiple_of(scale_width * input_width				, multiple=_lowerCamelCase  )

							return (new_height, new_width)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BILINEAR,			__a = False,			__a = 1,			__a = True,			__a = 1 / 255,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 384, "width": 384}
														_lowerCAmelCase     :							Optional[int]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[Any]    =    do_resize
														_lowerCAmelCase     :							Dict    =    size
														_lowerCAmelCase     :							Any    =    keep_aspect_ratio
														_lowerCAmelCase     :							str    =    ensure_multiple_of
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Dict    =    do_rescale
														_lowerCAmelCase     :							Optional[int]    =    rescale_factor
														_lowerCAmelCase     :							Dict    =    do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_lowerCAmelCase     :							int    =    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    snake_case__      (   self,			__a,			__a,			__a = False,			__a = 1,			__a = PILImageResampling.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
														_lowerCAmelCase     :							List[Any]    =    get_resize_output_image_size(
														    __a,			output_size=(size["height"], size["width"]),			keep_aspect_ratio=__a,			multiple=__a,			)
														return resize(__a,			size=__a,			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return rescale(__a,			scale=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return normalize(__a,			mean=__a,			std=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							List[Any]    =    size if size is not None else self.size
														_lowerCAmelCase     :							str    =    get_size_dict(__a)
														_lowerCAmelCase     :							Dict    =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
														_lowerCAmelCase     :							Any    =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Union[str, Any]    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Tuple    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							List[str]    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							List[str]    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Optional[Any]    =    make_list_of_images(__a)

														if not valid_images(__a):
																					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_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     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							Any    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_rescale:
																					_lowerCAmelCase     :							List[str]    =    [self.rescale(image=__a,			scale=__a) for image in images]

														if do_normalize:
																					_lowerCAmelCase     :							Dict    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[str]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							Optional[Any]    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(__a) != len(__a):
																												raise ValueError(
																												    "Make sure that you pass in as many target sizes as the batch dimension of the logits")

																					if is_torch_tensor(__a):
																												_lowerCAmelCase     :							List[Any]    =    target_sizes.numpy()

																					_lowerCAmelCase     :							Dict    =    []

																					for idx in range(len(__a)):
																												_lowerCAmelCase     :							int    =    torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0),			size=target_sizes[idx],			mode="bilinear",			align_corners=__a)
																												_lowerCAmelCase     :							int    =    resized_logits[0].argmax(dim=0)
																												semantic_segmentation.append(__a)
														else:
																					_lowerCAmelCase     :							Dict    =    logits.argmax(dim=1)
																					_lowerCAmelCase     :							str    =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

														return semantic_segmentation
