






'''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        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = "cpu"    ,	_SCREAMING_SNAKE_CASE = "openai/clip-vit-large-patch14"   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      device
        UpperCamelCase       						=      CLIPTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      [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]
        UpperCamelCase       						=      [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]
        UpperCamelCase       						=      torchvision.transforms.Normalize(self.image_mean    ,	self.image_std   )
        UpperCamelCase       						=      torchvision.transforms.Resize(224   )
        UpperCamelCase       						=      torchvision.transforms.CenterCrop(224   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.resize(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.center_crop(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.normalize(_SCREAMING_SNAKE_CASE   )
        return images


  def __call__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






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






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=0.0_1    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="image"    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=False    ,	)						->   None:



        """simple docstring"""






        super().__init__()
        UpperCamelCase       						=      None
        UpperCamelCase       						=      device if device else get_device()
        if vqgan:
              UpperCamelCase       						=      vqgan
        else:
              UpperCamelCase       						=      load_vqgan(self.device    ,	conf_path=_SCREAMING_SNAKE_CASE    ,	ckpt_path=_SCREAMING_SNAKE_CASE   )
        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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=True   )						->   Union[str, Any]:



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE   ):
              raise ValueError(
                  """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
                  """ function?)"""   )
        if len(_SCREAMING_SNAKE_CASE   ) == 1:
              print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""   )
        UpperCamelCase       						=      total_duration / len(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      [frame_duration] * len(_SCREAMING_SNAKE_CASE   )
        if extend_frames:
              UpperCamelCase       						=      1.5
              UpperCamelCase       						=      3
        for file_name in paths:
              if file_name.endswith(""".png"""   ):
                    images.append(imageio.imread(_SCREAMING_SNAKE_CASE   )   )
        imageio.mimsave(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	duration=_SCREAMING_SNAKE_CASE   )
        print(F"gif saved to {output_path}"   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None   )						->   str:



        """simple docstring"""






        if not (path or img):
              raise ValueError("""Input either path or tensor"""   )
        if img is not None:
              raise NotImplementedError
        UpperCamelCase       						=      preprocess(Image.open(_SCREAMING_SNAKE_CASE   )    ,	target_image_size=256   ).to(self.device   )
        UpperCamelCase       						=      preprocess_vqgan(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.vqgan.encode(_SCREAMING_SNAKE_CASE   )
        return z

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=None   )						->   Union[str, Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






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

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[str]:



        """simple docstring"""






        wandb.init(reinit=_SCREAMING_SNAKE_CASE    ,	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(_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      image.resize((256, 256)   )
              wandb.log("""Original Image"""    ,	wandb.Image(_SCREAMING_SNAKE_CASE   )   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   Union[str, Any]:



        """simple docstring"""






        if image_path:
              UpperCamelCase       						=      self._get_latent(_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      torch.randn(self.latent_dim    ,	device=self.device   )
        if self.log:
              self._init_logging(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

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

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

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








'''simple docstring'''

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand()







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand(args.accelerate_config_file					)






class        a_		(    lowerCamelCase       ):

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      parser.add_parser("""env"""   )
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )
        download_parser.add_argument(
            """--accelerate-config_file"""    ,	default=_SCREAMING_SNAKE_CASE    ,	help="""The accelerate config file to use for the default values in the launching script."""    ,	)
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      accelerate_config_file

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      """not installed"""
        if is_safetensors_available():
              import safetensors

              UpperCamelCase       						=      safetensors.__version__
        elif importlib.util.find_spec("""safetensors"""   ) is not None:
              import safetensors

              UpperCamelCase       						=      F"{safetensors.__version__} but is ignored because of PyTorch version too old."

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      UpperCamelCase       						=      """not found"""
        if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              UpperCamelCase       						=      accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      load_config_from_file(self._accelerate_config_file   ).to_dict()

              UpperCamelCase       						=      (
                  """\n""".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]   )
                  if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                  else F"\t{accelerate_config}"
              )

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_torch_available():
              import torch

              UpperCamelCase       						=      torch.__version__
              UpperCamelCase       						=      torch.cuda.is_available()

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_tf_available():
              import tensorflow as tf

              UpperCamelCase       						=      tf.__version__
              try:
                    # deprecated in v2.1
                    UpperCamelCase       						=      tf.test.is_gpu_available()
              except AttributeError:
                    # returns list of devices, convert to bool
                    UpperCamelCase       						=      bool(tf.config.list_physical_devices("""GPU"""   )   )

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_flax_available():
              import flax
              import jax
              import jaxlib

              UpperCamelCase       						=      flax.__version__
              UpperCamelCase       						=      jax.__version__
              UpperCamelCase       						=      jaxlib.__version__
              UpperCamelCase       						=      jax.lib.xla_bridge.get_backend().platform

        UpperCamelCase       						=      {
            """`transformers` version""": version,
            """Platform""": platform.platform(),
            """Python version""": platform.python_version(),
            """Huggingface_hub version""": huggingface_hub.__version__,
            """Safetensors version""": F"{safetensors_version}",
            """Accelerate version""": F"{accelerate_version}",
            """Accelerate config""": F"{accelerate_config_str}",
            """PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
            """Tensorflow version (GPU?)""": F"{tf_version} ({tf_cuda_available})",
            """Flax version (CPU?/GPU?/TPU?)""": F"{flax_version} ({jax_backend})",
            """Jax version""": F"{jax_version}",
            """JaxLib version""": F"{jaxlib_version}",
            """Using GPU in script?""": """<fill in>""",
            """Using distributed or parallel set-up in script?""": """<fill in>""",
        }

        print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""   )
        print(self.format_dict(_SCREAMING_SNAKE_CASE   )   )

        return info


  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"
