'''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"						):
    __UpperCAmelCase								=       device
    __UpperCAmelCase								=       CLIPTokenizerFast.from_pretrained(_lowercase						)
    __UpperCAmelCase								=       [0.48145466, 0.4578275, 0.40821073]
    __UpperCAmelCase								=       [0.26862954, 0.26130258, 0.27577711]
    __UpperCAmelCase								=       torchvision.transforms.Normalize(self.image_mean      ,self.image_std						)
    __UpperCAmelCase								=       torchvision.transforms.Resize(224						)
    __UpperCAmelCase								=       torchvision.transforms.CenterCrop(224						)





   def       snake_case						(				self:     Optional[int]      ,a:     Any						):
    __UpperCAmelCase								=       self.resize(_lowercase						)
    __UpperCAmelCase								=       self.center_crop(_lowercase						)
    __UpperCAmelCase								=       self.normalize(_lowercase						)
    return images





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




class 	_lowercase(  nn.Module					):



   """simple docstring"""





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

    __UpperCAmelCase								=       iterations
    __UpperCAmelCase								=       lr
    __UpperCAmelCase								=       log
    __UpperCAmelCase								=       make_grid
    __UpperCAmelCase								=       return_val
    __UpperCAmelCase								=       quantize
    __UpperCAmelCase								=       self.vqgan.decoder.z_shape





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





   def       snake_case						(				self:     Optional[Any]      ,a:     int=None      ,a:     Optional[Any]=None						):
    if not (path or img):
     raise ValueError('Input either path or tensor'						)
    if img is not None:
     raise NotImplementedError
    __UpperCAmelCase								=       preprocess(Image.open(_lowercase						)      ,target_image_size=256						).to(self.device						)
    __UpperCAmelCase								=       preprocess_vqgan(_lowercase						)
    __UpperCAmelCase								=       self.vqgan.encode(_lowercase						)
    return z





   def       snake_case						(				self:     List[str]      ,a:     Optional[int]						):
    __UpperCAmelCase								=       self.latent.detach().requires_grad_()
    __UpperCAmelCase								=       base_latent + transform_vector
    if self.quantize:
     __UpperCAmelCase								=       self.vqgan.quantize(_lowercase						)
    else:
     __UpperCAmelCase								=       trans_latent
    return self.vqgan.decode(_lowercase						)





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





   def       snake_case						(				self:     Union[str, Any]      ,a:     Tuple      ,a:     Tuple      ,a:     Union[str, Any]						):
    __UpperCAmelCase								=       self._get_clip_similarity(pos_prompts['prompts']      ,_lowercase      ,weights=(1 / pos_prompts['weights'])						)
    if neg_prompts:
     __UpperCAmelCase								=       self._get_clip_similarity(neg_prompts['prompts']      ,_lowercase      ,weights=neg_prompts['weights']						)
    else:
     __UpperCAmelCase								=       torch.tensor([1]      ,device=self.device						)
    __UpperCAmelCase								=       -torch.log(_lowercase						) + torch.log(_lowercase						)
    return loss





   def       snake_case						(				self:     List[Any]      ,a:     Tuple      ,a:     Any      ,a:     List[str]						):
    __UpperCAmelCase								=       torch.randn_like(self.latent      ,requires_grad=_lowercase      ,device=self.device						)
    __UpperCAmelCase								=       torch.optim.Adam([vector]      ,lr=self.lr						)

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





   def       snake_case						(				self:     Dict      ,a:     Union[str, Any]      ,a:     Tuple      ,a:     Optional[int]						):
    wandb.init(reinit=_lowercase      ,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:
     __UpperCAmelCase								=       Image.open(_lowercase						)
     __UpperCAmelCase								=       image.resize((256, 256)						)
     wandb.log('Original Image'      ,wandb.Image(_lowercase						)						)





   def       snake_case						(				self:     Any      ,a:     Optional[Any]						):
    if not prompts:
     return []
    __UpperCAmelCase								=       []
    __UpperCAmelCase								=       []
    if isinstance(_lowercase      ,_lowercase						):
     __UpperCAmelCase								=       [prompt.strip() for prompt in prompts.split('|'						)]
    for prompt in prompts:
     if isinstance(_lowercase      ,(tuple, list)						):
      __UpperCAmelCase								=       prompt[0]
      __UpperCAmelCase								=       float(prompt[1]						)
     elif ":" in prompt:
      __UpperCAmelCase								=       prompt.split(':'						)
      __UpperCAmelCase								=       float(_lowercase						)
     else:
      __UpperCAmelCase								=       prompt
      __UpperCAmelCase								=       1.0
     processed_prompts.append(_lowercase						)
     weights.append(_lowercase						)
    return {
        "prompts": processed_prompts,
        "weights": torch.tensor(_lowercase      ,device=self.device						),
    }





   def       snake_case						(				self:     Any      ,a:     List[Any]      ,a:     str=None      ,a:     List[str]=None      ,a:     int=True      ,a:     Dict=False      ,a:     Optional[int]=True      ,a:     int=True      ,a:     str=None      ,):
    if image_path:
     __UpperCAmelCase								=       self._get_latent(_lowercase						)
    else:
     __UpperCAmelCase								=       torch.randn(self.latent_dim      ,device=self.device						)
    if self.log:
     self._init_logging(_lowercase      ,_lowercase      ,_lowercase						)

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

    __UpperCAmelCase								=       self.vqgan.decode(self.latent						)[0]
    if show_intermediate:
     print('Original Image'						)
     show_pil(custom_to_pil(_lowercase						)						)

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








"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = VQModel(
									    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1E-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModel(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									snake_case   :			Tuple						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			Any						    = image[0, -3:, -3:, -1]
									snake_case   :			Any						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



