






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							:      Optional[Any]			,						UpperCamelCase_							:      str = "cpu"			,						UpperCamelCase_							:      str = "openai/clip-vit-large-patch14"					):


         """simple docstring"""



         __A       =						device
         __A       =						CLIPTokenizerFast.from_pretrained(UpperCamelCase_					)
         __A       =						[0.48145466, 0.4578275, 0.40821073]
         __A       =						[0.26862954, 0.26130258, 0.27577711]
         __A       =						torchvision.transforms.Normalize(self.image_mean			,						self.image_std					)
         __A       =						torchvision.transforms.Resize(224					)
         __A       =						torchvision.transforms.CenterCrop(224					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.resize(UpperCamelCase_					)
         __A       =						self.center_crop(UpperCamelCase_					)
         __A       =						self.normalize(UpperCamelCase_					)
         return images





     def __call__(					self							:      Optional[Any]			,						UpperCamelCase_							:      Dict=None			,						UpperCamelCase_							:      List[str]=None			,						**UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



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


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




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


         """simple docstring"""



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

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




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      int=None			,						UpperCamelCase_							:      Any=5			,						UpperCamelCase_							:      List[Any]=True					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      List[str]=None					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.latent.detach().requires_grad_()
         __A       =						base_latent + transform_vector
         if self.quantize:
             __A     ,						*__A       =						self.vqgan.quantize(UpperCamelCase_					)
         else:
             __A       =						trans_latent
         return self.vqgan.decode(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



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

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




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         wandb.init(reinit=UpperCamelCase_			,						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       =						Image.open(UpperCamelCase_					)
             __A       =						image.resize((256, 256)					)
             wandb.log("""Original Image"""			,						wandb.Image(UpperCamelCase_					)					)




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



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





     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[int]=None			,						UpperCamelCase_							:      int=None			,						UpperCamelCase_							:      Optional[Any]=True			,						UpperCamelCase_							:      Union[str, Any]=False			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      str=True			,						UpperCamelCase_							:      Optional[Any]=None			,						):


         """simple docstring"""



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

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

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

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




