




"""simple docstring"""


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 YolosImageProcessor






class 							lowercase(  unittest.TestCase				):







 '''simple docstring'''





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




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
   _snake_case							:	Dict			=						parent
   _snake_case							:	Any			=						batch_size
   _snake_case							:	str			=						num_channels
   _snake_case							:	List[Any]			=						min_resolution
   _snake_case							:	Tuple			=						max_resolution
   _snake_case							:	Union[str, Any]			=						do_resize
   _snake_case							:	List[str]			=						size
   _snake_case							:	Union[str, Any]			=						do_normalize
   _snake_case							:	List[Any]			=						image_mean
   _snake_case							:	List[str]			=						image_std
   _snake_case							:	Optional[int]			=						do_rescale
   _snake_case							:	List[str]			=						rescale_factor
   _snake_case							:	List[Any]			=						do_pad


 def 				UpperCamelCase_     (       self:	int	):




   '''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 				UpperCamelCase_     (       self:	Any,   a_:	Any,   a_:	Tuple=False	):




   '''simple docstring'''



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

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

   return expected_height, expected_width







@require_torch
@require_vision
class 							lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      YolosImageProcessor if is_vision_available() else None


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						YolosImageProcessingTester(self	)


 @property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						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_,   """size"""	)	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class.from_dict(self.image_processor_dict	)
   self.assertEqual(image_processor.size,   {"""shortest_edge""": 18, """longest_edge""": 1_333}	)
   self.assertEqual(image_processor.do_pad,   a_	)

   _snake_case							:	Dict			=						self.image_processing_class.from_dict(
       self.image_processor_dict,   size=42,   max_size=84,   pad_and_return_pixel_mask=a_	)
   self.assertEqual(image_processor.size,   {"""shortest_edge""": 42, """longest_edge""": 84}	)
   self.assertEqual(image_processor.do_pad,   a_	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.image_processing_class(**self.image_processor_dict	)
   # create random PIL images
   _snake_case							:	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
   _snake_case							:	Optional[Any]			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values

   _snake_case							:	Dict			=						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
   _snake_case							:	Dict			=						self.image_processor_tester.get_expected_values(a_,   batched=a_	)

   _snake_case							:	Any			=						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 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class(**self.image_processor_dict	)
   # create random numpy tensors
   _snake_case							:	List[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
   _snake_case							:	Any			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values

   _snake_case							:	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
   _snake_case							:	Tuple			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values

   _snake_case							:	Optional[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,
       ),   )


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						self.image_processing_class(**self.image_processor_dict	)
   # create random PyTorch tensors
   _snake_case							:	List[Any]			=						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
   _snake_case							:	Dict			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values

   _snake_case							:	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
   _snake_case							:	List[str]			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values

   _snake_case							:	List[str]			=						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 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.image_processing_class(**self.image_processor_dict	)
   _snake_case							:	Dict			=						self.image_processing_class(do_resize=a_,   do_normalize=a_,   do_rescale=a_	)
   # create random PyTorch tensors
   _snake_case							:	Union[str, Any]			=						prepare_image_inputs(self.image_processor_tester,   equal_resolution=a_,   torchify=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   torch.Tensor	)

   # Test whether the method "pad" and calling the image processor return the same tensors
   _snake_case							:	int			=						image_processing_a.pad(a_,   return_tensors="""pt"""	)
   _snake_case							:	int			=						image_processing_a(a_,   return_tensors="""pt"""	)

   self.assertTrue(
       torch.allclose(encoded_images_with_method["""pixel_values"""],   encoded_images["""pixel_values"""],   atol=1E-4	)	)


 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



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

   _snake_case							:	int			=						{"""image_id""": 39_769, """annotations""": target}

   # encode them
   _snake_case							:	Optional[Any]			=						YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""	)
   _snake_case							:	int			=						image_processing(images=a_,   annotations=a_,   return_tensors="""pt"""	)

   # verify pixel values
   _snake_case							:	Any			=						torch.Size([1, 3, 800, 1_066]	)
   self.assertEqual(encoding["""pixel_values"""].shape,   a_	)

   _snake_case							:	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
   _snake_case							:	List[str]			=						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
   _snake_case							:	Union[str, Any]			=						torch.Size([6, 4]	)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,   a_	)
   _snake_case							:	Union[str, 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
   _snake_case							:	Dict			=						torch.tensor([39_769]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],   a_	)	)
   # verify is_crowd
   _snake_case							:	Optional[int]			=						torch.tensor([0, 0, 0, 0, 0, 0]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],   a_	)	)
   # verify class_labels
   _snake_case							:	Union[str, Any]			=						torch.tensor([75, 75, 63, 65, 17, 17]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],   a_	)	)
   # verify orig_size
   _snake_case							:	Union[str, Any]			=						torch.tensor([480, 640]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],   a_	)	)
   # verify size
   _snake_case							:	Tuple			=						torch.tensor([800, 1_066]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],   a_	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



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

   _snake_case							:	Optional[Any]			=						{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

   _snake_case							:	List[Any]			=						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""	)

   # encode them
   _snake_case							:	List[Any]			=						YolosImageProcessor(format="""coco_panoptic"""	)
   _snake_case							:	str			=						image_processing(images=a_,   annotations=a_,   masks_path=a_,   return_tensors="""pt"""	)

   # verify pixel values
   _snake_case							:	Union[str, Any]			=						torch.Size([1, 3, 800, 1_066]	)
   self.assertEqual(encoding["""pixel_values"""].shape,   a_	)

   _snake_case							:	Union[str, Any]			=						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
   _snake_case							:	Any			=						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
   _snake_case							:	int			=						torch.Size([6, 4]	)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,   a_	)
   _snake_case							:	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
   _snake_case							:	int			=						torch.tensor([39_769]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],   a_	)	)
   # verify is_crowd
   _snake_case							:	Optional[int]			=						torch.tensor([0, 0, 0, 0, 0, 0]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],   a_	)	)
   # verify class_labels
   _snake_case							:	Optional[int]			=						torch.tensor([17, 17, 63, 75, 75, 93]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],   a_	)	)
   # verify masks
   _snake_case							:	Tuple			=						822_873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item(),   a_	)
   # verify orig_size
   _snake_case							:	Optional[int]			=						torch.tensor([480, 640]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],   a_	)	)
   # verify size
   _snake_case							:	List[Any]			=						torch.tensor([800, 1_066]	)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],   a_	)	)








"""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:	List[Any],   a_:	str = "cpu",   a_:	str = "openai/clip-vit-large-patch14"	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	List[str],   a_:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.resize(a_	)
   _snake_case							:	List[Any]			=						self.center_crop(a_	)
   _snake_case							:	Optional[Any]			=						self.normalize(a_	)
   return images


 def __call__(       self:	Any,   a_:	Optional[int]=None,   a_:	str=None,   **a_:	str	):




   '''simple docstring'''



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







class       lowercase(  nn.Module				):







 '''simple docstring'''





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




   '''simple docstring'''



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

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


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	str=None,   a_:	Dict=None,   a_:	Dict=5,   a_:	Dict=True	):




   '''simple docstring'''



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




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.latent.detach().requires_grad_()
   _snake_case							:	Tuple			=						base_latent + transform_vector
   if self.quantize:
     _snake_case	,      *_snake_case							:	Any			=						self.vqgan.quantize(a_	)
   else:
     _snake_case							:	List[Any]			=						trans_latent
   return self.vqgan.decode(a_	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Any,   a_:	Union[str, Any],   a_:	Dict=None	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.clip_preprocessor(text=a_,   images=a_,   return_tensors="""pt""",   padding=a_	)
   _snake_case							:	Any			=						self.clip(**a_	)
   _snake_case							:	str			=						clip_outputs.logits_per_image
   if weights is not None:
     _snake_case							:	Any			=						similarity_logits * weights
   return similarity_logits.sum()


 def 				UpperCamelCase_     (       self:	Any,   a_:	Any,   a_:	List[str],   a_:	Dict	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Tuple,   a_:	Union[str, Any],   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						torch.randn_like(self.latent,   requires_grad=a_,   device=self.device	)
   _snake_case							:	Dict			=						torch.optim.Adam([vector],   lr=self.lr	)

   for i in range(self.iterations	):
     optim.zero_grad()
     _snake_case							:	str			=						self._add_vector(a_	)
     _snake_case							:	List[Any]			=						loop_post_process(a_	)
     _snake_case							:	List[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 				UpperCamelCase_     (       self:	int,   a_:	Any,   a_:	Union[str, Any],   a_:	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:
     _snake_case							:	Any			=						Image.open(a_	)
     _snake_case							:	str			=						image.resize((256, 256)	)
     wandb.log("""Original Image""",   wandb.Image(a_	)	)


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any]	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Dict,   a_:	List[Any],   a_:	List[Any]=None,   a_:	Optional[Any]=None,   a_:	Optional[Any]=True,   a_:	Dict=False,   a_:	Optional[Any]=True,   a_:	Optional[Any]=True,   a_:	Any=None,   ):




   '''simple docstring'''



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

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

   _snake_case							:	List[Any]			=						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"	)	)


