





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							:







  def __init__(      self  :Optional[Any]   ,							_lowercase  :str = "cpu"   ,							_lowercase  :str = "openai/clip-vit-large-patch14" ):






     '''simple docstring'''





     lowercase__			       =  device
     lowercase__			       =  CLIPTokenizerFast.from_pretrained(_lowercase )
     lowercase__			       =  [0.48145466, 0.4578275, 0.40821073]
     lowercase__			       =  [0.26862954, 0.26130258, 0.27577711]
     lowercase__			       =  torchvision.transforms.Normalize(self.image_mean   ,							self.image_std )
     lowercase__			       =  torchvision.transforms.Resize(2_24 )
     lowercase__			       =  torchvision.transforms.CenterCrop(2_24 )







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.resize(_lowercase )
     lowercase__			       =  self.center_crop(_lowercase )
     lowercase__			       =  self.normalize(_lowercase )
     return images







  def __call__(      self  :Any   ,							_lowercase  :List[Any]=None   ,							_lowercase  :Any=None   ,							**_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  self.tokenizer(text=_lowercase   ,							**_lowercase )
     lowercase__			       =  self.preprocess_img(_lowercase )
     lowercase__			       =  {key: value.to(self.device ) for (key, value) in encoding.items()}
     return encoding





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Optional[Any]   ,							_lowercase  :List[Any]=10   ,							_lowercase  :int=0.01   ,							_lowercase  :Tuple=None   ,							_lowercase  :Any=None   ,							_lowercase  :Optional[int]=None   ,							_lowercase  :List[Any]=None   ,							_lowercase  :Tuple=None   ,							_lowercase  :Tuple=None   ,							_lowercase  :List[Any]=False   ,							_lowercase  :Optional[Any]=True   ,							_lowercase  :Dict="image"   ,							_lowercase  :Any=True   ,							_lowercase  :Tuple=False   ,							_lowercase  :str=False   ,							_lowercase  :int=False   ,							):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  None
     lowercase__			       =  device if device else get_device()
     if vqgan:
        lowercase__			       =  vqgan
     else:
        lowercase__			       =  load_vqgan(self.device   ,							conf_path=_lowercase   ,							ckpt_path=_lowercase )
     self.vqgan.eval()
     if clip:
        lowercase__			       =  clip
     else:
        lowercase__			       =  CLIPModel.from_pretrained("openai/clip-vit-base-patch32" )
     self.clip.to(self.device )
     lowercase__			       =  ProcessorGradientFlow(device=self.device )

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







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :Tuple=None   ,							_lowercase  :List[str]=None   ,							_lowercase  :int=5   ,							_lowercase  :Optional[int]=True ):






     '''simple docstring'''





     lowercase__			       =  []
     if output_path is None:
        lowercase__			       =  """./animation.gif"""
     if input_path is None:
        lowercase__			       =  self.save_path
     lowercase__			       =  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?)" )
     lowercase__			       =  total_duration / len(_lowercase )
     lowercase__			       =  [frame_duration] * len(_lowercase )
     if extend_frames:
        lowercase__			       =  1.5
        lowercase__			       =  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   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :List[Any]=None   ,							_lowercase  :int=None ):






     '''simple docstring'''





     if not (path or img):
        raise ValueError("Input either path or tensor" )
     if img is not None:
        raise NotImplementedError
     lowercase__			       =  preprocess(Image.open(_lowercase )   ,							target_image_size=2_56 ).to(self.device )
     lowercase__			       =  preprocess_vqgan(_lowercase )
     lowercase__			       =  self.vqgan.encode(_lowercase )
     return z







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  self.latent.detach().requires_grad_()
     lowercase__			       =  base_latent + transform_vector
     if self.quantize:
        lowercase__			       =  self.vqgan.quantize(_lowercase )
     else:
        lowercase__			       =  trans_latent
     return self.vqgan.decode(_lowercase )







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Dict   ,							_lowercase  :Dict   ,							_lowercase  :Tuple=None ):






     '''simple docstring'''





     lowercase__			       =  self.clip_preprocessor(text=_lowercase   ,							images=_lowercase   ,							return_tensors="pt"   ,							padding=_lowercase )
     lowercase__			       =  self.clip(**_lowercase )
     lowercase__			       =  clip_outputs.logits_per_image
     if weights is not None:
        lowercase__			       =  similarity_logits * weights
     return similarity_logits.sum()







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Dict   ,							_lowercase  :Dict   ,							_lowercase  :Any ):






     '''simple docstring'''





     lowercase__			       =  self._get_clip_similarity(pos_prompts["prompts"]   ,							_lowercase   ,							weights=(1 / pos_prompts["weights"]) )
     if neg_prompts:
        lowercase__			       =  self._get_clip_similarity(neg_prompts["prompts"]   ,							_lowercase   ,							weights=neg_prompts["weights"] )
     else:
        lowercase__			       =  torch.tensor([1]   ,							device=self.device )
     lowercase__			       =  -torch.log(_lowercase ) + torch.log(_lowercase )
     return loss







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :str   ,							_lowercase  :Optional[int]   ,							_lowercase  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  torch.randn_like(self.latent   ,							requires_grad=_lowercase   ,							device=self.device )
     lowercase__			       =  torch.optim.Adam([vector]   ,							lr=self.lr )

     for i in range(self.iterations ):
        optim.zero_grad()
        lowercase__			       =  self._add_vector(_lowercase )
        lowercase__			       =  loop_post_process(_lowercase )
        lowercase__			       =  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   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Any   ,							_lowercase  :str   ,							_lowercase  :int ):






     '''simple docstring'''





     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:
        lowercase__			       =  Image.open(_lowercase )
        lowercase__			       =  image.resize((2_56, 2_56) )
        wandb.log("Original Image"   ,							wandb.Image(_lowercase ) )







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :str ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Dict   ,							_lowercase  :Tuple=None   ,							_lowercase  :int=None   ,							_lowercase  :Optional[int]=True   ,							_lowercase  :Union[str, Any]=False   ,							_lowercase  :Union[str, Any]=True   ,							_lowercase  :Union[str, Any]=True   ,							_lowercase  :Dict=None   ,							):






     '''simple docstring'''





     if image_path:
        lowercase__			       =  self._get_latent(_lowercase )
     else:
        lowercase__			       =  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."
     lowercase__			       =  self.process_prompts(_lowercase )
     lowercase__			       =  self.process_prompts(_lowercase )
     if save_final and save_path is None:
        lowercase__			       =  os.path.join("./outputs/"   ,							"_".join(pos_prompts["prompts"] ) )
     if not os.path.exists(_lowercase ):
        os.makedirs(_lowercase )
     else:
        lowercase__			       =  save_path + """_""" + get_timestamp()
        os.makedirs(_lowercase )
     lowercase__			       =  save_path

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

     lowercase__			       =  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''' ) )













import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
  import torch


if is_vision_available():
  from PIL import Image

  from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class  lowerCAmelCase							(       unittest.TestCase ):
  __lowerCamelCase		=      ViTImageProcessor if is_vision_available() else None







  @property
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     return self.image_processor_tester.prepare_image_processor_dict()







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  (3, 32, 1_28)
     lowercase__			       =  tempfile.mkdtemp()

     # fmt: off
     lowercase__			       =  ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
     # fmt: on
     lowercase__			       =  dict(zip(_lowercase   ,							range(len(_lowercase ) ) ) )

     lowercase__			       =  os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES["vocab_file"] )
     with open(self.vocab_file   ,							"w"   ,							encoding="utf-8" ) as fp:
        fp.write(json.dumps(_lowercase ) + "\n" )

     lowercase__			       =  {
         "do_normalize": False,
         "do_resize": True,
         "image_processor_type": "ViTImageProcessor",
         "resample": 3,
         "size": {"height": 32, "width": 1_28},
     }
     lowercase__			       =  os.path.join(self.tmpdirname   ,							_lowercase )
     with open(self.image_processor_file   ,							"w"   ,							encoding="utf-8" ) as fp:
        json.dump(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							**_lowercase  :str ):






     '''simple docstring'''





     return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :List[Any]   ,							**_lowercase  :List[str] ):






     '''simple docstring'''





     return ViTImageProcessor.from_pretrained(self.tmpdirname   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     shutil.rmtree(self.tmpdirname )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  np.random.randint(2_55   ,							size=(3, 30, 4_00)   ,							dtype=np.uinta )

     lowercase__			       =  Image.fromarray(np.moveaxis(_lowercase   ,							0   ,							-1 ) )

     return image_input







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_tokenizer()
     lowercase__			       =  self.get_image_processor()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )
     lowercase__			       =  MgpstrProcessor.from_pretrained(self.tmpdirname   ,							use_fast=_lowercase )

     self.assertEqual(processor.char_tokenizer.get_vocab()   ,							tokenizer.get_vocab() )
     self.assertIsInstance(processor.char_tokenizer   ,							_lowercase )

     self.assertEqual(processor.image_processor.to_json_string()   ,							image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_tokenizer()
     lowercase__			       =  self.get_image_processor()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )

     lowercase__			       =  self.get_tokenizer(bos_token="(BOS)"   ,							eos_token="(EOS)" )
     lowercase__			       =  self.get_image_processor(do_normalize=_lowercase   ,							padding_value=1.0 )

     lowercase__			       =  MgpstrProcessor.from_pretrained(
         self.tmpdirname   ,							bos_token="(BOS)"   ,							eos_token="(EOS)"   ,							do_normalize=_lowercase   ,							padding_value=1.0 )

     self.assertEqual(processor.char_tokenizer.get_vocab()   ,							tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.char_tokenizer   ,							_lowercase )

     self.assertEqual(processor.image_processor.to_json_string()   ,							image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor   ,							_lowercase )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  image_processor(_lowercase   ,							return_tensors="np" )
     lowercase__			       =  processor(images=_lowercase   ,							return_tensors="np" )

     for key in input_image_proc.keys():
        self.assertAlmostEqual(input_image_proc[key].sum()   ,							input_processor[key].sum()   ,							delta=1e-2 )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  "test"

     lowercase__			       =  processor(text=_lowercase )

     lowercase__			       =  tokenizer(_lowercase )
     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]   ,							encoded_processor[key] )







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  "test"
     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  processor(text=_lowercase   ,							images=_lowercase )

     self.assertListEqual(list(inputs.keys() )   ,							["pixel_values", "labels"] )

     # test if it raises when no input is passed
     with pytest.raises(_lowercase ):
        processor()







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

     lowercase__			       =  processor.char_decode(_lowercase )
     lowercase__			       =  tokenizer.batch_decode(_lowercase )
     lowercase__			       =  [seq.replace(" "   ,							"" ) for seq in decoded_tok]

     self.assertListEqual(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  None
     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  processor(text=_lowercase   ,							images=_lowercase )

     self.assertListEqual(list(inputs.keys() )   ,							processor.model_input_names )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  torch.randn(1   ,							27   ,							38 )
     lowercase__			       =  torch.randn(1   ,							27   ,							5_02_57 )
     lowercase__			       =  torch.randn(1   ,							27   ,							3_05_22 )

     lowercase__			       =  processor.batch_decode([char_input, bpe_input, wp_input] )

     self.assertListEqual(list(results.keys() )   ,							["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"] )






