



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__:







    def __init__(				self			:  Optional[int]						,      __snake_case			:  str = "cpu"						,      __snake_case			:  str = "openai/clip-vit-large-patch14"     ):
      a						: List[Any]					       =					device
      a						: Optional[Any]					       =					CLIPTokenizerFast.from_pretrained(__a     )
      a						: Optional[Any]					       =					[0.48145466, 0.4578275, 0.40821073]
      a						: List[str]					       =					[0.26862954, 0.26130258, 0.27577711]
      a						: List[Any]					       =					torchvision.transforms.Normalize(self.image_mean						,      self.image_std     )
      a						: Optional[Any]					       =					torchvision.transforms.Resize(2_24     )
      a						: Optional[Any]					       =					torchvision.transforms.CenterCrop(2_24     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Optional[int]     ):
      a						: int					       =					self.resize(__a     )
      a						: Tuple					       =					self.center_crop(__a     )
      a						: List[Any]					       =					self.normalize(__a     )
      return images







    def __call__(				self			:  Optional[int]						,      __snake_case			:  str=None						,      __snake_case			:  List[str]=None						,      **__snake_case			:  Dict     ):
      a						: int					       =					self.tokenizer(text=__a						,      **__a     )
      a						: Optional[Any]					       =					self.preprocess_img(__a     )
      a						: List[Any]					       =					{key: value.to(self.device     ) for (key, value) in encoding.items()}
      return encoding

class 							a__( nn.Module     ):







    def __init__(				self			:  Dict						,      __snake_case			:  Tuple=10						,      __snake_case			:  Optional[int]=0.01						,      __snake_case			:  Tuple=None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[int]=None						,      __snake_case			:  Union[str, Any]=None						,      __snake_case			:  List[str]=None						,      __snake_case			:  Any=None						,      __snake_case			:  str=False						,      __snake_case			:  List[str]=True						,      __snake_case			:  List[Any]="image"						,      __snake_case			:  Optional[int]=True						,      __snake_case			:  List[Any]=False						,      __snake_case			:  List[str]=False						,      __snake_case			:  List[Any]=False						,      ):
      super().__init__()
      a						: Dict					       =					None
      a						: Any					       =					device if device else get_device()
      if vqgan:
        a						: Optional[Any]					       =					vqgan
      else:
        a						: Optional[Any]					       =					load_vqgan(self.device						,      conf_path=__a						,      ckpt_path=__a     )
      self.vqgan.eval()
      if clip:
        a						: List[Any]					       =					clip
      else:
        a						: List[str]					       =					CLIPModel.from_pretrained('openai/clip-vit-base-patch32'     )
      self.clip.to(self.device     )
      a						: Optional[Any]					       =					ProcessorGradientFlow(device=self.device     )

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







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Any=None						,      __snake_case			:  Union[str, Any]=None						,      __snake_case			:  Any=5						,      __snake_case			:  Optional[Any]=True     ):
      a						: str					       =					[]
      if output_path is None:
        a						: int					       =					'./animation.gif'
      if input_path is None:
        a						: Union[str, Any]					       =					self.save_path
      a						: 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?)'     )
      a						: Dict					       =					total_duration / len(__a     )
      a						: int					       =					[frame_duration] * len(__a     )
      if extend_frames:
        a						: Optional[int]					       =					1.5
        a						: List[str]					       =					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       lowercase_	(				self			:  List[str]						,      __snake_case			:  Dict=None						,      __snake_case			:  Any=None     ):
      if not (path or img):
        raise ValueError('Input either path or tensor'     )
      if img is not None:
        raise NotImplementedError
      a						: Tuple					       =					preprocess(Image.open(__a     )						,      target_image_size=2_56     ).to(self.device     )
      a						: Dict					       =					preprocess_vqgan(__a     )
      a       ,     *a						: int					       =					self.vqgan.encode(__a     )
      return z







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[Any]     ):
      a						: Tuple					       =					self.latent.detach().requires_grad_()
      a						: Union[str, Any]					       =					base_latent + transform_vector
      if self.quantize:
        a       ,     *a						: Dict					       =					self.vqgan.quantize(__a     )
      else:
        a						: Union[str, Any]					       =					trans_latent
      return self.vqgan.decode(__a     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Optional[int]						,      __snake_case			:  List[str]						,      __snake_case			:  Tuple=None     ):
      a						: Any					       =					self.clip_preprocessor(text=__a						,      images=__a						,      return_tensors='pt'						,      padding=__a     )
      a						: List[Any]					       =					self.clip(**__a     )
      a						: Any					       =					clip_outputs.logits_per_image
      if weights is not None:
        a						: List[Any]					       =					similarity_logits * weights
      return similarity_logits.sum()







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  int     ):
      a						: List[Any]					       =					self._get_clip_similarity(pos_prompts['prompts']						,      __a						,      weights=(1 / pos_prompts['weights'])     )
      if neg_prompts:
        a						: str					       =					self._get_clip_similarity(neg_prompts['prompts']						,      __a						,      weights=neg_prompts['weights']     )
      else:
        a						: Union[str, Any]					       =					torch.tensor([1]						,      device=self.device     )
      a						: Optional[int]					       =					-torch.log(__a     ) + torch.log(__a     )
      return loss







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Optional[int]						,      __snake_case			:  Tuple						,      __snake_case			:  Dict     ):
      a						: Any					       =					torch.randn_like(self.latent						,      requires_grad=__a						,      device=self.device     )
      a						: Dict					       =					torch.optim.Adam([vector]						,      lr=self.lr     )

      for i in range(self.iterations     ):
        optim.zero_grad()
        a						: Any					       =					self._add_vector(__a     )
        a						: List[Any]					       =					loop_post_process(__a     )
        a						: 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       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Any						,      __snake_case			:  Tuple     ):
      wandb.init(reinit=__a						,      project='face-editor'     )
      wandb.config.update({'Positive Prompts': positive_prompts}     )
      wandb.config.update({'Negative Prompts': negative_prompts}     )
      wandb.config.update({'lr': self.lr, 'iterations': self.iterations}     )
      if image_path:
        a						: Any					       =					Image.open(__a     )
        a						: List[str]					       =					image.resize((2_56, 2_56)     )
        wandb.log('Original Image'						,      wandb.Image(__a     )     )







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







    def       lowercase_	(				self			:  str						,      __snake_case			:  int						,      __snake_case			:  str=None						,      __snake_case			:  Dict=None						,      __snake_case			:  Tuple=True						,      __snake_case			:  Union[str, Any]=False						,      __snake_case			:  List[str]=True						,      __snake_case			:  Dict=True						,      __snake_case			:  Optional[Any]=None						,      ):
      if image_path:
        a						: str					       =					self._get_latent(__a     )
      else:
        a						: List[str]					       =					torch.randn(self.latent_dim						,      device=self.device     )
      if self.log:
        self._init_logging(__a						,      __a						,      __a     )

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

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

      a						: 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"""     )     )




'''simple docstring'''






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CycleDiffusionPipeline
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
    }
    lowercase__			=   PipelineTesterMixin.required_optional_params - {"""latents"""}
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_	(				self			:  Any     ):
      torch.manual_seed(0     )
      a						: Union[str, Any]					       =					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						,      )
      a						: str					       =					DDIMScheduler(
          beta_start=0.00085						,      beta_end=0.012						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=__snake_case						,      set_alpha_to_one=__snake_case						,      )
      torch.manual_seed(0     )
      a						: List[str]					       =					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						,      )
      torch.manual_seed(0     )
      a						: int					       =					CLIPTextConfig(
          bos_token_id=0						,      eos_token_id=2						,      hidden_size=32						,      intermediate_size=37						,      layer_norm_eps=1e-0_5						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=10_00						,      )
      a						: List[str]					       =					CLIPTextModel(__snake_case     )
      a						: int					       =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

      a						: Tuple					       =					{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Any=0     ):
      a						: str					       =					floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: Optional[Any]					       =					image / 2 + 0.5
      if str(__snake_case     ).startswith('mps'     ):
        a						: List[str]					       =					torch.manual_seed(__snake_case     )
      else:
        a						: Union[str, Any]					       =					torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
      a						: List[Any]					       =					{
          'prompt': 'An astronaut riding an elephant',
          'source_prompt': 'An astronaut riding a horse',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'eta': 0.1,
          'strength': 0.8,
          'guidance_scale': 3,
          'source_guidance_scale': 1,
          'output_type': 'numpy',
      }
      return inputs







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[Any]					       =					'cpu'  # ensure determinism for the device-dependent torch.Generator

      a						: int					       =					self.get_dummy_components()
      a						: str					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: List[str]					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: Dict					       =					self.get_dummy_inputs(__snake_case     )
      a						: Union[str, Any]					       =					pipe(**__snake_case     )
      a						: List[Any]					       =					output.images

      a						: Optional[Any]					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: Tuple					       =					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







    @unittest.skipIf(torch_device != 'cuda'						,      'This test requires a GPU'     )
    def       lowercase_	(				self			:  int     ):
      a						: List[Any]					       =					self.get_dummy_components()
      for name, module in components.items():
        if hasattr(__snake_case						,      'half'     ):
          a						: Any					       =					module.half()
      a						: Tuple					       =					CycleDiffusionPipeline(**__snake_case     )
      a						: Any					       =					pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: str					       =					self.get_dummy_inputs(__snake_case     )
      a						: int					       =					pipe(**__snake_case     )
      a						: Optional[int]					       =					output.images

      a						: Tuple					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: int					       =					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'     )
    def       lowercase_	(				self			:  Dict     ):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def       lowercase_	(				self			:  int     ):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_save_load_optional_components()







    @skip_mps
    def       lowercase_	(				self			:  List[Any]     ):
      return super().test_attention_slicing_forward_pass()

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







    def       lowercase_	(				self			:  Tuple     ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: Optional[int]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
      a						: List[str]					       =					init_image.resize((5_12, 5_12)     )

      a						: Dict					       =					'CompVis/stable-diffusion-v1-4'
      a						: List[str]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: Any					       =					CycleDiffusionPipeline.from_pretrained(
          __snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case						,      torch_dtype=torch.floataa						,      revision='fp16'     )

      pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )
      pipe.enable_attention_slicing()

      a						: Union[str, Any]					       =					'A black colored car'
      a						: Optional[Any]					       =					'A blue colored car'

      a						: int					       =					torch.manual_seed(0     )
      a						: Optional[Any]					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Dict					       =					output.images

      # the values aren't exactly equal, but the images look the same visually
      assert np.abs(image - expected_image     ).max() < 5e-1







    def       lowercase_	(				self			:  int     ):
      a						: Any					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: List[str]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
      a						: str					       =					init_image.resize((5_12, 5_12)     )

      a						: Optional[int]					       =					'CompVis/stable-diffusion-v1-4'
      a						: Union[str, Any]					       =					DDIMScheduler.from_pretrained(__snake_case						,      subfolder='scheduler'     )
      a						: str					       =					CycleDiffusionPipeline.from_pretrained(__snake_case						,      scheduler=__snake_case						,      safety_checker=__snake_case     )

      pipe.to(__snake_case     )
      pipe.set_progress_bar_config(disable=__snake_case     )
      pipe.enable_attention_slicing()

      a						: Tuple					       =					'A black colored car'
      a						: Tuple					       =					'A blue colored car'

      a						: List[str]					       =					torch.manual_seed(0     )
      a						: str					       =					pipe(
          prompt=__snake_case						,      source_prompt=__snake_case						,      image=__snake_case						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__snake_case						,      output_type='np'						,      )
      a						: Tuple					       =					output.images

      assert np.abs(image - expected_image     ).max() < 2e-2