


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DetaImageProcessor


class 					lowerCAmelCase_ (       unittest.TestCase):




						def __init__(	self						:   Tuple   ,     __A						:   Dict   ,     __A						:   Optional[Any]=7   ,     __A						:   Any=3   ,     __A						:   Dict=30   ,     __A						:   Dict=400   ,     __A						:   int=True   ,     __A						:   int=None   ,     __A						:   Union[str, Any]=True   ,     __A						:   List[str]=[0.5, 0.5, 0.5]   ,     __A						:   str=[0.5, 0.5, 0.5]   ,     __A						:   Optional[int]=True   ,     __A						:   Optional[Any]=1 / 255   ,     __A						:   str=True   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :str						  =    size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
									a__ :Union[str, Any]						  =    parent
									a__ :Dict						  =    batch_size
									a__ :Optional[int]						  =    num_channels
									a__ :Any						  =    min_resolution
									a__ :Optional[Any]						  =    max_resolution
									a__ :List[Any]						  =    do_resize
									a__ :Any						  =    size
									a__ :Any						  =    do_normalize
									a__ :str						  =    image_mean
									a__ :Any						  =    image_std
									a__ :Dict						  =    do_rescale
									a__ :Tuple						  =    rescale_factor
									a__ :int						  =    do_pad




						def  _snake_case					(	self						:   Tuple						)      ->Optional[Any]:






									"""simple docstring"""






									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}






						def  _snake_case					(	self						:   int   ,     __A						:   Any   ,     __A						:   Any=False						)      ->Tuple:






									"""simple docstring"""






									if not batched:
												a__ :List[Any]						  =    image_inputs[0]
												if isinstance(__A   ,     Image.Image						):
															a__  ,  a__ :int						  =    image.size
												else:
															a__  ,  a__ :int						  =    image.shape[1], image.shape[2]
												if w < h:
															a__ :Any						  =    int(self.size["shortest_edge"] * h / w						)
															a__ :Optional[Any]						  =    self.size["shortest_edge"]
												elif w > h:
															a__ :Optional[Any]						  =    self.size["shortest_edge"]
															a__ :str						  =    int(self.size["shortest_edge"] * w / h						)
												else:
															a__ :List[Any]						  =    self.size["shortest_edge"]
															a__ :Any						  =    self.size["shortest_edge"]

									else:
												a__ :int						  =    []
												for image in image_inputs:
															a__  ,  a__ :str						  =    self.get_expected_values([image]						)
															expected_values.append((expected_height, expected_width)						)
												a__ :Optional[Any]						  =    max(__A   ,     key=lambda __A						: item[0]						)[0]
												a__ :str						  =    max(__A   ,     key=lambda __A						: item[1]						)[1]

									return expected_height, expected_width






@require_torch
@require_vision
class 					lowerCAmelCase_ (       _a			,unittest.TestCase):
						lowerCamelCase_         =     DetaImageProcessor if is_vision_available() else None




						def  _snake_case					(	self						:   Optional[int]						)      ->str:






									"""simple docstring"""






									a__ :Tuple						  =    DetaImageProcessingTester(self						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Optional[Any]:






									"""simple docstring"""






									return self.image_processor_tester.prepare_image_processor_dict()




						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(__A   ,     "image_mean"						)						)
									self.assertTrue(hasattr(__A   ,     "image_std"						)						)
									self.assertTrue(hasattr(__A   ,     "do_normalize"						)						)
									self.assertTrue(hasattr(__A   ,     "do_resize"						)						)
									self.assertTrue(hasattr(__A   ,     "do_rescale"						)						)
									self.assertTrue(hasattr(__A   ,     "do_pad"						)						)
									self.assertTrue(hasattr(__A   ,     "size"						)						)




						def  _snake_case					(	self						:   Dict						)      ->Dict:






									"""simple docstring"""






									a__ :Tuple						  =    self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size   ,     {"shortest_edge": 18, "longest_edge": 1333}						)
									self.assertEqual(image_processor.do_pad   ,     __A						)




						def  _snake_case					(	self						:   List[Any]						)      ->int:






									"""simple docstring"""






									pass




						def  _snake_case					(	self						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									a__ :Optional[Any]						  =    prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=__A						)
									for image in image_inputs:
												self.assertIsInstance(__A   ,     Image.Image						)

									# Test not batched input
									a__ :List[str]						  =    image_processing(image_inputs[0]   ,     return_tensors="pt"						).pixel_values

									a__  ,  a__ :Any						  =    self.image_processor_tester.get_expected_values(__A						)

									self.assertEqual(
									    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

									# Test batched
									a__  ,  a__ :Tuple						  =    self.image_processor_tester.get_expected_values(__A   ,     batched=__A						)

									a__ :int						  =    image_processing(__A   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,     )




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Dict:






									"""simple docstring"""






									a__ :Tuple						  =    self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									a__ :Union[str, Any]						  =    prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=__A   ,     numpify=__A						)
									for image in image_inputs:
												self.assertIsInstance(__A   ,     np.ndarray						)

									# Test not batched input
									a__ :List[str]						  =    image_processing(image_inputs[0]   ,     return_tensors="pt"						).pixel_values

									a__  ,  a__ :List[Any]						  =    self.image_processor_tester.get_expected_values(__A						)

									self.assertEqual(
									    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

									# Test batched
									a__ :Optional[Any]						  =    image_processing(__A   ,     return_tensors="pt"						).pixel_values

									a__  ,  a__ :Tuple						  =    self.image_processor_tester.get_expected_values(__A   ,     batched=__A						)

									self.assertEqual(
									    encoded_images.shape   ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,     )




						def  _snake_case					(	self						:   str						)      ->Dict:






									"""simple docstring"""






									a__ :str						  =    self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									a__ :List[str]						  =    prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=__A   ,     torchify=__A						)
									for image in image_inputs:
												self.assertIsInstance(__A   ,     torch.Tensor						)

									# Test not batched input
									a__ :Optional[int]						  =    image_processing(image_inputs[0]   ,     return_tensors="pt"						).pixel_values

									a__  ,  a__ :List[str]						  =    self.image_processor_tester.get_expected_values(__A						)

									self.assertEqual(
									    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

									# Test batched
									a__ :Dict						  =    image_processing(__A   ,     return_tensors="pt"						).pixel_values

									a__  ,  a__ :Any						  =    self.image_processor_tester.get_expected_values(__A   ,     batched=__A						)

									self.assertEqual(
									    encoded_images.shape   ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,     )




						@slow
						def  _snake_case					(	self						:   List[Any]						)      ->int:






									"""simple docstring"""






									a__ :Any						  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
									with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,     "r"						) as f:
												a__ :Dict						  =    json.loads(f.read()						)

									a__ :int						  =    {"image_id": 39769, "annotations": target}

									# encode them
									a__ :Optional[int]						  =    DetaImageProcessor()
									a__ :int						  =    image_processing(images=__A   ,     annotations=__A   ,     return_tensors="pt"						)

									# verify pixel values
									a__ :Dict						  =    torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding["pixel_values"].shape   ,     __A						)

									a__ :Optional[int]						  =    torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,     __A   ,     atol=1E-4						)						)

									# verify area
									a__ :Any						  =    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,     __A						)						)
									# verify boxes
									a__ :int						  =    torch.Size([6, 4]						)
									self.assertEqual(encoding["labels"][0]["boxes"].shape   ,     __A						)
									a__ :Any						  =    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,     __A   ,     atol=1E-3						)						)
									# verify image_id
									a__ :str						  =    torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,     __A						)						)
									# verify is_crowd
									a__ :Optional[Any]						  =    torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,     __A						)						)
									# verify class_labels
									a__ :Optional[Any]						  =    torch.tensor([75, 75, 63, 65, 17, 17]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,     __A						)						)
									# verify orig_size
									a__ :str						  =    torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,     __A						)						)
									# verify size
									a__ :Any						  =    torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,     __A						)						)






						@slow
						def  _snake_case					(	self						:   int						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
									with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,     "r"						) as f:
												a__ :int						  =    json.loads(f.read()						)

									a__ :int						  =    {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

									a__ :int						  =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

									# encode them
									a__ :str						  =    DetaImageProcessor(format="coco_panoptic"						)
									a__ :List[Any]						  =    image_processing(images=__A   ,     annotations=__A   ,     masks_path=__A   ,     return_tensors="pt"						)

									# verify pixel values
									a__ :Union[str, Any]						  =    torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding["pixel_values"].shape   ,     __A						)

									a__ :str						  =    torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,     __A   ,     atol=1E-4						)						)

									# verify area
									a__ :int						  =    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,     __A						)						)
									# verify boxes
									a__ :Optional[Any]						  =    torch.Size([6, 4]						)
									self.assertEqual(encoding["labels"][0]["boxes"].shape   ,     __A						)
									a__ :str						  =    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,     __A   ,     atol=1E-3						)						)
									# verify image_id
									a__ :Any						  =    torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,     __A						)						)
									# verify is_crowd
									a__ :str						  =    torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,     __A						)						)
									# verify class_labels
									a__ :Optional[int]						  =    torch.tensor([17, 17, 63, 75, 75, 93]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,     __A						)						)
									# verify masks
									a__ :Optional[Any]						  =    822873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,     __A						)
									# verify orig_size
									a__ :Union[str, Any]						  =    torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,     __A						)						)
									# verify size
									a__ :Any						  =    torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,     __A						)						)






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_ :




						def __init__(	self						:   str   ,     __A						:   str = "cpu"   ,     __A						:   str = "openai/clip-vit-large-patch14"						)      ->None:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   int   ,     __A						:   int						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[str]						  =    self.resize(__A						)
									a__ :Union[str, Any]						  =    self.center_crop(__A						)
									a__ :List[str]						  =    self.normalize(__A						)
									return images






						def __call__(	self						:   Union[str, Any]   ,     __A						:   Dict=None   ,     __A						:   List[Any]=None   ,     **__A						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.tokenizer(text=__A   ,     **__A						)
									a__ :str						  =    self.preprocess_img(__A						)
									a__ :Any						  =    {key: value.to(self.device						) for (key, value) in encoding.items()}
									return encoding






class 					lowerCAmelCase_ (       nn.Module):




						def __init__(	self						:   List[str]   ,     __A						:   List[str]=10   ,     __A						:   List[Any]=0.01   ,     __A						:   Optional[int]=None   ,     __A						:   List[str]=None   ,     __A						:   Dict=None   ,     __A						:   List[str]=None   ,     __A						:   Tuple=None   ,     __A						:   str=None   ,     __A						:   Any=False   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple="image"   ,     __A						:   Tuple=True   ,     __A						:   int=False   ,     __A						:   Tuple=False   ,     __A						:   Union[str, Any]=False   ,     )      ->None:






									"""simple docstring"""






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

									a__ :Any						  =    iterations
									a__ :Any						  =    lr
									a__ :List[str]						  =    log
									a__ :List[Any]						  =    make_grid
									a__ :Optional[int]						  =    return_val
									a__ :Optional[Any]						  =    quantize
									a__ :Any						  =    self.vqgan.decoder.z_shape




						def  _snake_case					(	self						:   str   ,     __A						:   str=None   ,     __A						:   Optional[int]=None   ,     __A						:   int=5   ,     __A						:   Any=True						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    []
									if output_path is None:
												a__ :Any						  =    "./animation.gif"
									if input_path is None:
												a__ :Dict						  =    self.save_path
									a__ :Dict						  =    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?)"						)
									a__ :str						  =    total_duration / len(__A						)
									a__ :str						  =    [frame_duration] * len(__A						)
									if extend_frames:
												a__ :Optional[Any]						  =    1.5
												a__ :Optional[Any]						  =    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						:   str   ,     __A						:   Tuple=None   ,     __A						:   Optional[int]=None						)      ->Optional[int]:






									"""simple docstring"""






									if not (path or img):
												raise ValueError("Input either path or tensor"						)
									if img is not None:
												raise NotImplementedError
									a__ :List[Any]						  =    preprocess(Image.open(__A						)   ,     target_image_size=256						).to(self.device						)
									a__ :List[str]						  =    preprocess_vqgan(__A						)
									a__  ,  *a__ :Union[str, Any]						  =    self.vqgan.encode(__A						)
									return z




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Any						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Dict						  =    self.latent.detach().requires_grad_()
									a__ :int						  =    base_latent + transform_vector
									if self.quantize:
												a__  ,  *a__ :str						  =    self.vqgan.quantize(__A						)
									else:
												a__ :List[str]						  =    trans_latent
									return self.vqgan.decode(__A						)




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any]   ,     __A						:   List[Any]=None						)      ->str:






									"""simple docstring"""






									a__ :Dict						  =    self.clip_preprocessor(text=__A   ,     images=__A   ,     return_tensors="pt"   ,     padding=__A						)
									a__ :List[str]						  =    self.clip(**__A						)
									a__ :Optional[int]						  =    clip_outputs.logits_per_image
									if weights is not None:
												a__ :Tuple						  =    similarity_logits * weights
									return similarity_logits.sum()




						def  _snake_case					(	self						:   List[str]   ,     __A						:   List[Any]   ,     __A						:   Optional[int]   ,     __A						:   Optional[int]						)      ->int:






									"""simple docstring"""






									a__ :str						  =    self._get_clip_similarity(pos_prompts["prompts"]   ,     __A   ,     weights=(1 / pos_prompts["weights"])						)
									if neg_prompts:
												a__ :Optional[int]						  =    self._get_clip_similarity(neg_prompts["prompts"]   ,     __A   ,     weights=neg_prompts["weights"]						)
									else:
												a__ :Tuple						  =    torch.tensor([1]   ,     device=self.device						)
									a__ :Dict						  =    -torch.log(__A						) + torch.log(__A						)
									return loss




						def  _snake_case					(	self						:   int   ,     __A						:   Dict   ,     __A						:   Union[str, Any]   ,     __A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									a__ :List[Any]						  =    torch.randn_like(self.latent   ,     requires_grad=__A   ,     device=self.device						)
									a__ :Optional[Any]						  =    torch.optim.Adam([vector]   ,     lr=self.lr						)

									for i in range(self.iterations						):
												optim.zero_grad()
												a__ :Tuple						  =    self._add_vector(__A						)
												a__ :Dict						  =    loop_post_process(__A						)
												a__ :str						  =    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						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]						)      ->Optional[int]:






									"""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:
												a__ :List[str]						  =    Image.open(__A						)
												a__ :Any						  =    image.resize((256, 256)						)
												wandb.log("Original Image"   ,     wandb.Image(__A						)						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									if not prompts:
												return []
									a__ :List[str]						  =    []
									a__ :Tuple						  =    []
									if isinstance(__A   ,     __A						):
												a__ :Tuple						  =    [prompt.strip() for prompt in prompts.split("|"						)]
									for prompt in prompts:
												if isinstance(__A   ,     (tuple, list)						):
															a__ :str						  =    prompt[0]
															a__ :List[Any]						  =    float(prompt[1]						)
												elif ":" in prompt:
															a__  ,  a__ :Tuple						  =    prompt.split(":"						)
															a__ :Dict						  =    float(__A						)
												else:
															a__ :int						  =    prompt
															a__ :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						:   Optional[Any]   ,     __A						:   Any   ,     __A						:   int=None   ,     __A						:   int=None   ,     __A						:   str=True   ,     __A						:   List[Any]=False   ,     __A						:   Optional[int]=True   ,     __A						:   Any=True   ,     __A						:   Optional[Any]=None   ,     )      ->Optional[Any]:






									"""simple docstring"""






									if image_path:
												a__ :Optional[int]						  =    self._get_latent(__A						)
									else:
												a__ :Dict						  =    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."
									a__ :int						  =    self.process_prompts(__A						)
									a__ :Any						  =    self.process_prompts(__A						)
									if save_final and save_path is None:
												a__ :Dict						  =    os.path.join("./outputs/"   ,     "_".join(pos_prompts["prompts"]						)						)
									if not os.path.exists(__A						):
												os.makedirs(__A						)
									else:
												a__ :str						  =    save_path + "_" + get_timestamp()
												os.makedirs(__A						)
									a__ :Any						  =    save_path

									a__ :Optional[int]						  =    self.vqgan.decode(self.latent						)[0]
									if show_intermediate:
												print("Original Image"						)
												show_pil(custom_to_pil(__A						)						)

									a__ :List[str]						  =    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'''						)						)


