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 :       int  ,     __magic_name__ :       str = "cpu"  ,     __magic_name__ :       str = "openai/clip-vit-large-patch14"   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  device
        SCREAMING_SNAKE_CASE_          =  CLIPTokenizerFast.from_pretrained(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [0.4814_5466, 0.457_8275, 0.4082_1073]
        SCREAMING_SNAKE_CASE_          =  [0.2686_2954, 0.2613_0258, 0.2757_7711]
        SCREAMING_SNAKE_CASE_          =  torchvision.transforms.Normalize(self.image_mean  ,     self.image_std   )
        SCREAMING_SNAKE_CASE_          =  torchvision.transforms.Resize(224   )
        SCREAMING_SNAKE_CASE_          =  torchvision.transforms.CenterCrop(224   )







  def    __A     (							self :       int  ,     __magic_name__ :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.resize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.center_crop(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.normalize(__magic_name__   )
        return images







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



class   lowerCamelCase					(nn.Module					):
  """simple docstring"""







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

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







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







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







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.latent.detach().requires_grad_()
        SCREAMING_SNAKE_CASE_          =  base_latent + transform_vector
        if self.quantize:
              SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  self.vqgan.quantize(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  trans_latent
        return self.vqgan.decode(__magic_name__   )







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[Any]=None   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.clip_preprocessor(text=__magic_name__  ,     images=__magic_name__  ,     return_tensors="pt"  ,     padding=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.clip(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  clip_outputs.logits_per_image
        if weights is not None:
              SCREAMING_SNAKE_CASE_          =  similarity_logits * weights
        return similarity_logits.sum()







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







  def    __A     (							self :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  torch.randn_like(self.latent  ,     requires_grad=__magic_name__  ,     device=self.device   )
        SCREAMING_SNAKE_CASE_          =  torch.optim.Adam([vector]  ,     lr=self.lr   )

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







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Any   )  ->	Optional[int]:
        wandb.init(reinit=__magic_name__  ,     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_          =  Image.open(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  image.resize((256, 256)   )
              wandb.log("Original Image"  ,     wandb.Image(__magic_name__   )   )







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







  def    __A     (							self :       int  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       str=True  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       List[str]=True  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       Any=None  ,     )  ->	Dict:
        if image_path:
              SCREAMING_SNAKE_CASE_          =  self._get_latent(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  torch.randn(self.latent_dim  ,     device=self.device   )
        if self.log:
              self._init_logging(__magic_name__  ,     __magic_name__  ,     __magic_name__   )

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

        SCREAMING_SNAKE_CASE_          =  self.vqgan.decode(self.latent   )[0]
        if show_intermediate:
              print("Original Image"   )
              show_pil(custom_to_pil(__magic_name__   )   )

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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


