




"""simple docstring"""


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	List[str],   a_:	List[Any],   a_:	str=13,   a_:	Dict=32,   a_:	Union[str, Any]=3,   a_:	Union[str, Any]=4,   a_:	Tuple=[10, 20, 30, 40],   a_:	Dict=[2, 2, 3, 2],   a_:	Tuple=True,   a_:	Optional[Any]=True,   a_:	Any=37,   a_:	Any="gelu",   a_:	int=10,   a_:	Tuple=0.02,   a_:	str=["stage2", "stage3", "stage4"],   a_:	List[str]=[2, 3, 4],   a_:	List[str]=None,   ):




   '''simple docstring'''



   _snake_case							:	int			=						parent
   _snake_case							:	int			=						batch_size
   _snake_case							:	List[Any]			=						image_size
   _snake_case							:	List[str]			=						num_channels
   _snake_case							:	Tuple			=						num_stages
   _snake_case							:	Union[str, Any]			=						hidden_sizes
   _snake_case							:	List[Any]			=						depths
   _snake_case							:	Tuple			=						is_training
   _snake_case							:	List[str]			=						use_labels
   _snake_case							:	Tuple			=						intermediate_size
   _snake_case							:	List[str]			=						hidden_act
   _snake_case							:	Optional[Any]			=						num_labels
   _snake_case							:	Tuple			=						initializer_range
   _snake_case							:	Tuple			=						out_features
   _snake_case							:	Tuple			=						out_indices
   _snake_case							:	Dict			=						scope


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	Any			=						None
   if self.use_labels:
     _snake_case							:	Dict			=						ids_tensor([self.batch_size],   self.num_labels	)

   _snake_case							:	Optional[Any]			=						self.get_config()

   return config, pixel_values, labels


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return ConvNextVaConfig(
       num_channels=self.num_channels,   hidden_sizes=self.hidden_sizes,   depths=self.depths,   num_stages=self.num_stages,   hidden_act=self.hidden_act,   is_decoder=a_,   initializer_range=self.initializer_range,   out_features=self.out_features,   out_indices=self.out_indices,   num_labels=self.num_labels,   )


 def 				UpperCamelCase_     (       self:	int,   a_:	Tuple,   a_:	Any,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						ConvNextVaModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Any			=						model(a_	)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),   )


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




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						ConvNextVaForImageClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Optional[int]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)	)


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




   '''simple docstring'''



   _snake_case							:	List[str]			=						ConvNextVaBackbone(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						model(a_	)

   # verify hidden states
   self.parent.assertEqual(len(result.feature_maps	),   len(config.out_features	)	)
   self.parent.assertListEqual(list(result.feature_maps[0].shape	),   [self.batch_size, self.hidden_sizes[1], 4, 4]	)

   # verify channels
   self.parent.assertEqual(len(model.channels	),   len(config.out_features	)	)
   self.parent.assertListEqual(model.channels,   config.hidden_sizes[1:]	)

   # verify backbone works with out_features=None
   _snake_case							:	Tuple			=						None
   _snake_case							:	Tuple			=						ConvNextVaBackbone(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[Any]			=						model(a_	)

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps	),   1	)
   self.parent.assertListEqual(list(result.feature_maps[0].shape	),   [self.batch_size, self.hidden_sizes[-1], 1, 1]	)

   # verify channels
   self.parent.assertEqual(len(model.channels	),   1	)
   self.parent.assertListEqual(model.channels,   [config.hidden_sizes[-1]]	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	Any			=						config_and_inputs
   _snake_case							:	str			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	List[str]			=						config_and_inputs
   _snake_case							:	Any			=						{"""pixel_values""": pixel_values, """labels""": labels}
   return config, inputs_dict







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







 '''simple docstring'''





 lowercase__				      =      (
     (
         ConvNextVaModel,
         ConvNextVaForImageClassification,
         ConvNextVaBackbone,
     )
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						ConvNextVaModelTester(self	)
   _snake_case							:	int			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return


 @unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""	)
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""	)
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   for model_class in self.all_model_classes:
     _snake_case	,      _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_with_labels()
     _snake_case							:	List[Any]			=						True

     if model_class.__name__ in [
         *get_values(a_	),
         *get_values(a_	),
     ]:
       continue

     _snake_case							:	Tuple			=						model_class(a_	)
     model.to(a_	)
     model.train()
     _snake_case							:	Optional[Any]			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	Any			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   for model_class in self.all_model_classes:
     _snake_case	,      _snake_case							:	str			=						self.model_tester.prepare_config_and_inputs_with_labels()
     _snake_case							:	Any			=						False
     _snake_case							:	List[Any]			=						True

     if (
         model_class.__name__
         in [*get_values(a_	), *get_values(a_	)]
         or not model_class.supports_gradient_checkpointing
     ):
       continue

     _snake_case							:	Dict			=						model_class(a_	)
     model.to(a_	)
     model.gradient_checkpointing_enable()
     model.train()
     _snake_case							:	str			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	Optional[int]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	List[str]			=						model_class(a_	)
     _snake_case							:	Union[str, Any]			=						inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:	int			=						[*signature.parameters.keys()]

     _snake_case							:	Union[str, Any]			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


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




   '''simple docstring'''



   def check_hidden_states_output(a_:	str,   a_:	Tuple,   a_:	Tuple	):
     _snake_case							:	Optional[Any]			=						model_class(a_	)
     model.to(a_	)
     model.eval()

     with torch.no_grad():
       _snake_case							:	Any			=						model(**self._prepare_for_class(a_,   a_	)	)

     _snake_case							:	Optional[int]			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _snake_case							:	Optional[int]			=						self.model_tester.num_stages
     self.assertEqual(len(a_	),   expected_num_stages + 1	)

     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	),   [self.model_tester.image_size // 4, self.model_tester.image_size // 4],   )

   _snake_case	,      _snake_case							:	Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	Optional[Any]			=						True
     check_hidden_states_output(a_,   a_,   a_	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _snake_case							:	List[str]			=						True

     check_hidden_states_output(a_,   a_,   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	str			=						ConvNextVaModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	List[Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image






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







 '''simple docstring'''





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




   '''simple docstring'''



   return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""	) if is_vision_available() else None


 @slow
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""	).to(a_	)

   _snake_case							:	Union[str, Any]			=						self.default_image_processor
   _snake_case							:	List[Any]			=						prepare_img()
   _snake_case							:	Optional[int]			=						preprocessor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)

   # verify the logits
   _snake_case							:	Optional[int]			=						torch.Size((1, 1_000)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	Optional[int]			=						torch.tensor([0.9_996, 0.1_966, -0.4_386]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, :3],   a_,   atol=1E-4	)	)








"""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"	)	)


