



'''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 	lowerCAmelCase_:
		'''simple docstring'''




		def __init__(   self  ,__UpperCAmelCase = "cpu"  ,__UpperCAmelCase = "openai/clip-vit-large-patch14"				)   ->		None:
						lowerCAmelCase__			:  Optional[Any]											= device
						lowerCAmelCase__			:  str											= CLIPTokenizerFast.from_pretrained(__UpperCAmelCase				)
						lowerCAmelCase__			:  str											= [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
						lowerCAmelCase__			:  Tuple											= [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
						lowerCAmelCase__			:  List[Any]											= torchvision.transforms.Normalize(self.image_mean  ,self.image_std				)
						lowerCAmelCase__			:  Any											= torchvision.transforms.Resize(224				)
						lowerCAmelCase__			:  List[Any]											= torchvision.transforms.CenterCrop(224				)




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[str]:
						lowerCAmelCase__			:  List[str]											= self.resize(__UpperCAmelCase				)
						lowerCAmelCase__			:  int											= self.center_crop(__UpperCAmelCase				)
						lowerCAmelCase__			:  Tuple											= self.normalize(__UpperCAmelCase				)
						return images






		def __call__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		Any:
						lowerCAmelCase__			:  Tuple											= self.tokenizer(text=__UpperCAmelCase  ,**__UpperCAmelCase				)
						lowerCAmelCase__			:  Optional[int]											= self.preprocess_img(__UpperCAmelCase				)
						lowerCAmelCase__			:  Union[str, Any]											= {key: value.to(self.device				) for (key, value) in encoding.items()}
						return encoding







class 	lowerCAmelCase_(    nn.Module					):
		'''simple docstring'''




		def __init__(   self  ,__UpperCAmelCase=10  ,__UpperCAmelCase=0.0_1  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True  ,__UpperCAmelCase="image"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,)   ->		None:
						super().__init__()
						lowerCAmelCase__			:  int											= None
						lowerCAmelCase__			:  List[str]											= device if device else get_device()
						if vqgan:
										lowerCAmelCase__			:  Dict											= vqgan
						else:
										lowerCAmelCase__			:  str											= load_vqgan(self.device  ,conf_path=__UpperCAmelCase  ,ckpt_path=__UpperCAmelCase				)
						self.vqgan.eval()
						if clip:
										lowerCAmelCase__			:  List[Any]											= clip
						else:
										lowerCAmelCase__			:  Any											= CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""				)
						self.clip.to(self.device				)
						lowerCAmelCase__			:  Optional[Any]											= ProcessorGradientFlow(device=self.device				)

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




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=5  ,__UpperCAmelCase=True				)   ->		List[str]:
						lowerCAmelCase__			:  Optional[Any]											= []
						if output_path is None:
										lowerCAmelCase__			:  int											= """./animation.gif"""
						if input_path is None:
										lowerCAmelCase__			:  List[Any]											= self.save_path
						lowerCAmelCase__			:  Optional[Any]											= sorted(glob(input_path + """/*"""				)				)
						if not len(__UpperCAmelCase				):
										raise ValueError(
										    """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
										    """ function?)"""				)
						if len(__UpperCAmelCase				) == 1:
										print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""				)
						lowerCAmelCase__			:  str											= total_duration / len(__UpperCAmelCase				)
						lowerCAmelCase__			:  Tuple											= [frame_duration] * len(__UpperCAmelCase				)
						if extend_frames:
										lowerCAmelCase__			:  Tuple											= 1.5
										lowerCAmelCase__			:  Tuple											= 3
						for file_name in paths:
										if file_name.endswith(""".png"""				):
														images.append(imageio.imread(__UpperCAmelCase				)				)
						imageio.mimsave(__UpperCAmelCase  ,__UpperCAmelCase  ,duration=__UpperCAmelCase				)
						print(F"""gif saved to {output_path}"""				)




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None				)   ->		int:
						if not (path or img):
										raise ValueError("""Input either path or tensor"""				)
						if img is not None:
										raise NotImplementedError
						lowerCAmelCase__			:  List[str]											= preprocess(Image.open(__UpperCAmelCase				)  ,target_image_size=256				).to(self.device				)
						lowerCAmelCase__			:  Optional[int]											= preprocess_vqgan(__UpperCAmelCase				)
						lowerCAmelCase__			:  Any											= self.vqgan.encode(__UpperCAmelCase				)
						return z




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		int:
						lowerCAmelCase__			:  int											= self.latent.detach().requires_grad_()
						lowerCAmelCase__			:  Dict											= base_latent + transform_vector
						if self.quantize:
										lowerCAmelCase__			:  List[str]											= self.vqgan.quantize(__UpperCAmelCase				)
						else:
										lowerCAmelCase__			:  str											= trans_latent
						return self.vqgan.decode(__UpperCAmelCase				)




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  Any											= self.clip_preprocessor(text=__UpperCAmelCase  ,images=__UpperCAmelCase  ,return_tensors="""pt"""  ,padding=__UpperCAmelCase				)
						lowerCAmelCase__			:  Dict											= self.clip(**__UpperCAmelCase				)
						lowerCAmelCase__			:  Optional[int]											= clip_outputs.logits_per_image
						if weights is not None:
										lowerCAmelCase__			:  Optional[Any]											= similarity_logits * weights
						return similarity_logits.sum()




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  Tuple											= self._get_clip_similarity(pos_prompts["""prompts"""]  ,__UpperCAmelCase  ,weights=(1 / pos_prompts["""weights"""])				)
						if neg_prompts:
										lowerCAmelCase__			:  Optional[int]											= self._get_clip_similarity(neg_prompts["""prompts"""]  ,__UpperCAmelCase  ,weights=neg_prompts["""weights"""]				)
						else:
										lowerCAmelCase__			:  List[Any]											= torch.tensor([1]  ,device=self.device				)
						lowerCAmelCase__			:  str											= -torch.log(__UpperCAmelCase				) + torch.log(__UpperCAmelCase				)
						return loss




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Any:
						lowerCAmelCase__			:  List[str]											= torch.randn_like(self.latent  ,requires_grad=__UpperCAmelCase  ,device=self.device				)
						lowerCAmelCase__			:  Dict											= torch.optim.Adam([vector]  ,lr=self.lr				)

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




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		str:
						wandb.init(reinit=__UpperCAmelCase  ,project="""face-editor"""				)
						wandb.config.update({"""Positive Prompts""": positive_prompts}				)
						wandb.config.update({"""Negative Prompts""": negative_prompts}				)
						wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}				)
						if image_path:
										lowerCAmelCase__			:  Any											= Image.open(__UpperCAmelCase				)
										lowerCAmelCase__			:  List[str]											= image.resize((256, 256)				)
										wandb.log("""Original Image"""  ,wandb.Image(__UpperCAmelCase				)				)




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






		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,)   ->		Optional[int]:
						if image_path:
										lowerCAmelCase__			:  Optional[int]											= self._get_latent(__UpperCAmelCase				)
						else:
										lowerCAmelCase__			:  List[str]											= torch.randn(self.latent_dim  ,device=self.device				)
						if self.log:
										self._init_logging(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)

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

						lowerCAmelCase__			:  Optional[Any]											= self.vqgan.decode(self.latent				)[0]
						if show_intermediate:
										print("""Original Image"""				)
										show_pil(custom_to_pil(__UpperCAmelCase				)				)

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







'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs




  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available()  ,reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,)
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


