import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class   __lowercase       (     _A							,  _A							,  unittest.TestCase     ):
						lowercase       			=  StableDiffusionDiffEditPipeline
						lowercase       			=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
						lowercase       			=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
						lowercase       			=  frozenset(
						    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						lowercase       			=  frozenset([]     )

						def 							__a		(						self					:   Tuple )				->		Union[str, Any]:


									'''simple docstring'''

									torch.manual_seed(0 )
									lowercase       		=		UNetaDConditionModel(
									    block_out_channels=(32, 64)				,				layers_per_block=2				,				sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,				cross_attention_dim=32				,				attention_head_dim=(2, 4)				,				use_linear_projection=__lowerCamelCase				,				)
									lowercase       		=		DDIMScheduler(
									    beta_start=0.0_0085				,				beta_end=0.012				,				beta_schedule='''scaled_linear'''				,				clip_sample=__lowerCamelCase				,				set_alpha_to_one=__lowerCamelCase				,				)
									lowercase       		=		DDIMInverseScheduler(
									    beta_start=0.0_0085				,				beta_end=0.012				,				beta_schedule='''scaled_linear'''				,				clip_sample=__lowerCamelCase				,				set_alpha_to_zero=__lowerCamelCase				,				)
									torch.manual_seed(0 )
									lowercase       		=		AutoencoderKL(
									    block_out_channels=[32, 64]				,				in_channels=3				,				out_channels=3				,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,				latent_channels=4				,				sample_size=1_28				,				)
									torch.manual_seed(0 )
									lowercase       		=		CLIPTextConfig(
									    bos_token_id=0				,				eos_token_id=2				,				hidden_size=32				,				intermediate_size=37				,				layer_norm_eps=1E-05				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=10_00				,				hidden_act='''gelu'''				,				projection_dim=5_12				,				)
									lowercase       		=		CLIPTextModel(__lowerCamelCase )
									lowercase       		=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

									lowercase       		=		{
									    'unet': unet,
									    'scheduler': scheduler,
									    'inverse_scheduler': inverse_scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}

									return components

						def 							__a		(						self					:   Optional[int]				,				__lowerCamelCase					:   Any				,				__lowerCamelCase					:   Union[str, Any]=0 )				->		Union[str, Any]:


									'''simple docstring'''

									lowercase       		=		floats_tensor((1, 16, 16)				,				rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									lowercase       		=		floats_tensor((1, 2, 4, 16, 16)				,				rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									if str(__lowerCamelCase ).startswith('''mps''' ):
												lowercase       		=		torch.manual_seed(__lowerCamelCase )
									else:
												lowercase       		=		torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
									lowercase       		=		{
									    'prompt': 'a dog and a newt',
									    'mask_image': mask,
									    'image_latents': latents,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'inpaint_strength': 1.0,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}

									return inputs

						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   str				,				__lowerCamelCase					:   Any=0 )				->		Any:


									'''simple docstring'''

									lowercase       		=		floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									lowercase       		=		image.cpu().permute(0				,				2				,				3				,				1 )[0]
									lowercase       		=		Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' )
									if str(__lowerCamelCase ).startswith('''mps''' ):
												lowercase       		=		torch.manual_seed(__lowerCamelCase )
									else:
												lowercase       		=		torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
									lowercase       		=		{
									    'image': image,
									    'source_prompt': 'a cat and a frog',
									    'target_prompt': 'a dog and a newt',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'num_maps_per_mask': 2,
									    'mask_encode_strength': 1.0,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}

									return inputs

						def 							__a		(						self					:   Optional[int]				,				__lowerCamelCase					:   Dict				,				__lowerCamelCase					:   Tuple=0 )				->		Any:


									'''simple docstring'''

									lowercase       		=		floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									lowercase       		=		image.cpu().permute(0				,				2				,				3				,				1 )[0]
									lowercase       		=		Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' )
									if str(__lowerCamelCase ).startswith('''mps''' ):
												lowercase       		=		torch.manual_seed(__lowerCamelCase )
									else:
												lowercase       		=		torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
									lowercase       		=		{
									    'image': image,
									    'prompt': 'a cat and a frog',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'inpaint_strength': 1.0,
									    'guidance_scale': 6.0,
									    'decode_latents': True,
									    'output_type': 'numpy',
									}
									return inputs

						def 							__a		(						self					:   str )				->		List[Any]:


									'''simple docstring'''

									if not hasattr(self.pipeline_class				,				'''_optional_components''' ):
												return

									lowercase       		=		self.get_dummy_components()
									lowercase       		=		self.pipeline_class(**__lowerCamelCase )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									# set all optional components to None and update pipeline config accordingly
									for optional_component in pipe._optional_components:
												setattr(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase )
									pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )

									lowercase       		=		self.get_dummy_inputs(__lowerCamelCase )
									lowercase       		=		pipe(**__lowerCamelCase )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(__lowerCamelCase )
												lowercase       		=		self.pipeline_class.from_pretrained(__lowerCamelCase )
												pipe_loaded.to(__lowerCamelCase )
												pipe_loaded.set_progress_bar_config(disable=__lowerCamelCase )

									for optional_component in pipe._optional_components:
												self.assertTrue(
												    getattr(__lowerCamelCase				,				__lowerCamelCase ) is None				,				f'`{optional_component}` did not stay set to None after loading.'				,				)

									lowercase       		=		self.get_dummy_inputs(__lowerCamelCase )
									lowercase       		=		pipe_loaded(**__lowerCamelCase )[0]

									lowercase       		=		np.abs(output - output_loaded ).max()
									self.assertLess(__lowerCamelCase				,				1E-4 )

						def 							__a		(						self					:   Tuple )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		'cpu'

									lowercase       		=		self.get_dummy_components()
									lowercase       		=		self.pipeline_class(**__lowerCamelCase )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									lowercase       		=		self.get_dummy_mask_inputs(__lowerCamelCase )
									lowercase       		=		pipe.generate_mask(**__lowerCamelCase )
									lowercase       		=		mask[0, -3:, -3:]

									self.assertEqual(mask.shape				,				(1, 16, 16) )
									lowercase       		=		np.array([0] * 9 )
									lowercase       		=		np.abs(mask_slice.flatten() - expected_slice ).max()
									self.assertLessEqual(__lowerCamelCase				,				1E-3 )
									self.assertEqual(mask[0, -3, -4]				,				0 )

						def 							__a		(						self					:   Any )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		'cpu'

									lowercase       		=		self.get_dummy_components()
									lowercase       		=		self.pipeline_class(**__lowerCamelCase )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									lowercase       		=		self.get_dummy_inversion_inputs(__lowerCamelCase )
									lowercase       		=		pipe.invert(**__lowerCamelCase ).images
									lowercase       		=		image[0, -1, -3:, -3:]

									self.assertEqual(image.shape				,				(2, 32, 32, 3) )
									lowercase       		=		np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]				,				)
									lowercase       		=		np.abs(image_slice.flatten() - expected_slice ).max()
									self.assertLessEqual(__lowerCamelCase				,				1E-3 )

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


									'''simple docstring'''

									super().test_inference_batch_single_identical(expected_max_diff=5E-3 )



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


									'''simple docstring'''

									lowercase       		=		'cpu'

									lowercase       		=		self.get_dummy_components()

									lowercase       		=		{'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
									lowercase       		=		DPMSolverMultistepScheduler(**__lowerCamelCase )
									lowercase       		=		DPMSolverMultistepInverseScheduler(**__lowerCamelCase )

									lowercase       		=		self.pipeline_class(**__lowerCamelCase )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									lowercase       		=		self.get_dummy_inversion_inputs(__lowerCamelCase )
									lowercase       		=		pipe.invert(**__lowerCamelCase ).images
									lowercase       		=		image[0, -1, -3:, -3:]

									self.assertEqual(image.shape				,				(2, 32, 32, 3) )
									lowercase       		=		np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]				,				)
									lowercase       		=		np.abs(image_slice.flatten() - expected_slice ).max()
									self.assertLessEqual(__lowerCamelCase				,				1E-3 )






@require_torch_gpu
@slow
class   __lowercase       (     unittest.TestCase     ):

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


									'''simple docstring'''

									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

						@classmethod
						def 							__a		(						cls					:   Union[str, Any] )				->		Optional[Any]:


									'''simple docstring'''

									lowercase       		=		load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )

									lowercase       		=		raw_image.convert('''RGB''' ).resize((7_68, 7_68) )

									lowercase       		=		raw_image

						def 							__a		(						self					:   Optional[int] )				->		Optional[Any]:


									'''simple docstring'''

									lowercase       		=		torch.manual_seed(0 )

									lowercase       		=		StableDiffusionDiffEditPipeline.from_pretrained(
									    '''stabilityai/stable-diffusion-2-1'''				,				safety_checker=__lowerCamelCase				,				torch_dtype=torch.floataa )
									lowercase       		=		DDIMScheduler.from_config(pipe.scheduler.config )
									lowercase       		=		DDIMInverseScheduler.from_config(pipe.scheduler.config )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									lowercase       		=		'a bowl of fruit'
									lowercase       		=		'a bowl of pears'

									lowercase       		=		pipe.generate_mask(
									    image=self.raw_image				,				source_prompt=__lowerCamelCase				,				target_prompt=__lowerCamelCase				,				generator=__lowerCamelCase				,				)

									lowercase       		=		pipe.invert(
									    prompt=__lowerCamelCase				,				image=self.raw_image				,				inpaint_strength=0.7				,				generator=__lowerCamelCase ).latents

									lowercase       		=		pipe(
									    prompt=__lowerCamelCase				,				mask_image=__lowerCamelCase				,				image_latents=__lowerCamelCase				,				generator=__lowerCamelCase				,				negative_prompt=__lowerCamelCase				,				inpaint_strength=0.7				,				output_type='''numpy'''				,				).images[0]

									lowercase       		=		(
									    np.array(
									        load_image(
									            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									            '''/diffedit/pears.png''' ).resize((7_68, 7_68) ) )
									    / 2_55
									)
									assert np.abs((expected_image - image).max() ) < 5E-1



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


									'''simple docstring'''

									lowercase       		=		torch.manual_seed(0 )

									lowercase       		=		StableDiffusionDiffEditPipeline.from_pretrained(
									    '''stabilityai/stable-diffusion-2-1'''				,				safety_checker=__lowerCamelCase				,				torch_dtype=torch.floataa )
									lowercase       		=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
									lowercase       		=		DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									lowercase       		=		'a bowl of fruit'
									lowercase       		=		'a bowl of pears'

									lowercase       		=		pipe.generate_mask(
									    image=self.raw_image				,				source_prompt=__lowerCamelCase				,				target_prompt=__lowerCamelCase				,				generator=__lowerCamelCase				,				)

									lowercase       		=		pipe.invert(
									    prompt=__lowerCamelCase				,				image=self.raw_image				,				inpaint_strength=0.7				,				generator=__lowerCamelCase				,				num_inference_steps=25				,				).latents

									lowercase       		=		pipe(
									    prompt=__lowerCamelCase				,				mask_image=__lowerCamelCase				,				image_latents=__lowerCamelCase				,				generator=__lowerCamelCase				,				negative_prompt=__lowerCamelCase				,				inpaint_strength=0.7				,				num_inference_steps=25				,				output_type='''numpy'''				,				).images[0]

									lowercase       		=		(
									    np.array(
									        load_image(
									            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									            '''/diffedit/pears.png''' ).resize((7_68, 7_68) ) )
									    / 2_55
									)
									assert np.abs((expected_image - image).max() ) < 5E-1











'''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 							_a				:





       '''simple docstring'''






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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						device
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CLIPTokenizerFast.from_pretrained(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
             SCREAMING_SNAKE_CASE   :      str    					=						[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
             SCREAMING_SNAKE_CASE   :      Dict    					=						torchvision.transforms.Normalize(self.image_mean,							self.image_std  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						torchvision.transforms.Resize(224  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torchvision.transforms.CenterCrop(224  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.resize(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.center_crop(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.normalize(A  )
             return images







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







             '''simple docstring'''







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






class 							_a				(			nn.Module ):





       '''simple docstring'''






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

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







       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=5,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[]
             if output_path is None:
                   SCREAMING_SNAKE_CASE   :      int    					=						'./animation.gif'
             if input_path is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.save_path
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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?)'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						total_duration / len(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[frame_duration] * len(A  )
             if extend_frames:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						1.5
                   SCREAMING_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,							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
             SCREAMING_SNAKE_CASE   :      str    					=						preprocess(Image.open(A  ),							target_image_size=256  ).to(self.device  )
             SCREAMING_SNAKE_CASE   :      Any    					=						preprocess_vqgan(A  )
             SCREAMING_SNAKE_CASE					,    *SCREAMING_SNAKE_CASE   :      Tuple    					=						self.vqgan.encode(A  )
             return z







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.latent.detach().requires_grad_()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						base_latent + transform_vector
             if self.quantize:
                   SCREAMING_SNAKE_CASE					,    *SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.quantize(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						trans_latent
             return self.vqgan.decode(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A=None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.clip_preprocessor(text=A,							images=A,							return_tensors='pt',							padding=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.clip(**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						clip_outputs.logits_per_image
             if weights is not None:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						similarity_logits * weights
             return similarity_logits.sum()







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A=None,							A=None,							A=True,							A=False,							A=True,							A=True,							A=None,							):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.decode(self.latent  )[0]
             if show_intermediate:
                   print('Original Image'  )
                   show_pil(custom_to_pil(A  )  )

             SCREAMING_SNAKE_CASE   :      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"  )  )


