



"""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       SCREAMING_SNAKE_CASE__       :

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







      """simple docstring"""







      lowercase_	    =       device
      lowercase_	    =       CLIPTokenizerFast.from_pretrained(UpperCAmelCase__)
      lowercase_	    =       [0.48_145_466, 0.4_578_275, 0.40_821_073]
      lowercase_	    =       [0.26_862_954, 0.26_130_258, 0.27_577_711]
      lowercase_	    =       torchvision.transforms.Normalize(self.image_mean       ,     self.image_std)
      lowercase_	    =       torchvision.transforms.Resize(2_2_4)
      lowercase_	    =       torchvision.transforms.CenterCrop(2_2_4)

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







      """simple docstring"""







      lowercase_	    =       self.resize(UpperCAmelCase__)
      lowercase_	    =       self.center_crop(UpperCAmelCase__)
      lowercase_	    =       self.normalize(UpperCAmelCase__)
      return images




    def __call__( self	: Dict       ,     lowerCAmelCase_	: List[Any]=None       ,     lowerCAmelCase_	: List[Any]=None       ,     **lowerCAmelCase_	: Dict):







      """simple docstring"""







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







class       SCREAMING_SNAKE_CASE__       (							nn.Module      ):

    def __init__( self	: Optional[int]       ,     lowerCAmelCase_	: Union[str, Any]=1_0       ,     lowerCAmelCase_	: Optional[int]=0.01       ,     lowerCAmelCase_	: Any=None       ,     lowerCAmelCase_	: Optional[Any]=None       ,     lowerCAmelCase_	: Optional[int]=None       ,     lowerCAmelCase_	: Any=None       ,     lowerCAmelCase_	: List[Any]=None       ,     lowerCAmelCase_	: List[Any]=None       ,     lowerCAmelCase_	: Dict=False       ,     lowerCAmelCase_	: Union[str, Any]=True       ,     lowerCAmelCase_	: Optional[Any]="image"       ,     lowerCAmelCase_	: Tuple=True       ,     lowerCAmelCase_	: List[Any]=False       ,     lowerCAmelCase_	: Optional[int]=False       ,     lowerCAmelCase_	: Tuple=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=UpperCAmelCase__       ,     ckpt_path=UpperCAmelCase__)
      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	: int       ,     lowerCAmelCase_	: Optional[int]=None       ,     lowerCAmelCase_	: str=None       ,     lowerCAmelCase_	: Optional[Any]=5       ,     lowerCAmelCase_	: 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(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?)""")
      lowercase_	    =       total_duration / len(UpperCAmelCase__)
      lowercase_	    =       [frame_duration] * len(UpperCAmelCase__)
      if extend_frames:
        lowercase_	    =       1.5
        lowercase_	    =       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     _UpperCAmelCase		( self	: Dict       ,     lowerCAmelCase_	: Union[str, Any]=None       ,     lowerCAmelCase_	: Tuple=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(UpperCAmelCase__)       ,     target_image_size=2_5_6).to(self.device)
      lowercase_	    =       preprocess_vqgan(UpperCAmelCase__)
      lowercase_	    =       self.vqgan.encode(UpperCAmelCase__)
      return z

    def     _UpperCAmelCase		( self	: List[str]       ,     lowerCAmelCase_	: Dict):







      """simple docstring"""







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

    def     _UpperCAmelCase		( self	: Dict       ,     lowerCAmelCase_	: Union[str, Any]       ,     lowerCAmelCase_	: Any       ,     lowerCAmelCase_	: Tuple=None):







      """simple docstring"""







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

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







      """simple docstring"""







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

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







      """simple docstring"""







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

      for i in range(self.iterations):
        optim.zero_grad()
        lowercase_	    =       self._add_vector(UpperCAmelCase__)
        lowercase_	    =       loop_post_process(UpperCAmelCase__)
        lowercase_	    =       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     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: Tuple       ,     lowerCAmelCase_	: Union[str, Any]       ,     lowerCAmelCase_	: List[Any]):







      """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:
        lowercase_	    =       Image.open(UpperCAmelCase__)
        lowercase_	    =       image.resize((2_5_6, 2_5_6))
        wandb.log("""Original Image"""       ,     wandb.Image(UpperCAmelCase__))

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







      """simple docstring"""







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




    def     _UpperCAmelCase		( self	: Tuple       ,     lowerCAmelCase_	: Tuple       ,     lowerCAmelCase_	: Union[str, Any]=None       ,     lowerCAmelCase_	: List[str]=None       ,     lowerCAmelCase_	: List[str]=True       ,     lowerCAmelCase_	: List[str]=False       ,     lowerCAmelCase_	: List[Any]=True       ,     lowerCAmelCase_	: List[str]=True       ,     lowerCAmelCase_	: Optional[int]=None       ,     ):







      """simple docstring"""







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

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

      lowercase_	    =       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'''))









"""simple docstring"""




from __future__ import annotations

import time

import numpy as np

_snake_case          =   [8, 5, 9, 7]
_snake_case          =   [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_snake_case          =   [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class        UpperCamelCase			:


 def __init__(     self		:						List[Any]							,						UpperCAmelCase__		:						list[int]							,						UpperCAmelCase__		:						list[list[int]]							,						UpperCAmelCase__		:						list[list[int]]							,						)					->       None:
   _a  :       List[str]             =	claim_vector
   _a  :       List[Any]             =	allocated_resources_table
   _a  :       Union[str, Any]             =	maximum_claim_table


 def _lowercase	(     self		:						Tuple							)					->       list[int]:
   return [
       sum(p_item[i] for p_item in self.__allocated_resources_table							)
       for i in range(len(self.__allocated_resources_table[0]							)							)
   ]


 def _lowercase	(     self		:						int							)					->       list[int]:
   return np.array(self.__claim_vector							) - np.array(
       self.__processes_resource_summation()							)


 def _lowercase	(     self		:						List[str]							)					->       list[list[int]]:
   return [
       list(np.array(self.__maximum_claim_table[i]							) - np.array(UpperCAmelCase__							)							)
       for i, allocated_resource in enumerate(self.__allocated_resources_table							)
   ]


 def _lowercase	(     self		:						Optional[Any]							)					->       dict[int, list[int]]:
   return {self.__need().index(UpperCAmelCase__							): i for i in self.__need()}


 def _lowercase	(     self		:						Dict							,						**UpperCAmelCase__		:						Optional[Any]							)					->       None:
   _a  :       List[Any]             =	self.__need()
   _a  :       Optional[int]             =	self.__allocated_resources_table
   _a  :       str             =	self.__available_resources()
   _a  :       Optional[Any]             =	self.__need_index_manager()
   for kw, val in kwargs.items():
     if kw and val is True:
       self.__pretty_data()
   print("""_""" * 50 + """\n"""							)
   while need_list:
     _a  :       int             =	False
     for each_need in need_list:
       _a  :       Optional[int]             =	True
       for index, need in enumerate(UpperCAmelCase__							):
         if need > available_resources[index]:
           _a  :       List[Any]             =	False
           break
       if execution:
         _a  :       str             =	True
         # get the original index of the process from ind_ctrl db
         for original_need_index, need_clone in need_index_manager.items():
           if each_need == need_clone:
             _a  :       Any             =	original_need_index
         print(f"""Process {process_number + 1} is executing."""							)
         # remove the process run from stack
         need_list.remove(UpperCAmelCase__							)
         # update available/freed resources stack
         _a  :       Union[str, Any]             =	np.array(UpperCAmelCase__							) + np.array(
             alloc_resources_table[process_number]							)
         print(
             """Updated available resource stack for processes: """
             + """ """.join([str(UpperCAmelCase__							) for x in available_resources]							)							)
         break
     if safe:
       print("""The process is in a safe state.\n"""							)
     else:
       print("""System in unsafe state. Aborting...\n"""							)
       break







 def _lowercase	(     self		:						Any							)					->       Optional[int]:
   print(""" """ * 9 + """Allocated Resource Table"""							)
   for item in self.__allocated_resources_table:
     print(
         f"""P{self.__allocated_resources_table.index(UpperCAmelCase__							) + 1}"""
         + """ """.join(f"""{it:>8}""" for it in item							)
         + """\n"""							)
   print(""" """ * 9 + """System Resource Table"""							)
   for item in self.__maximum_claim_table:
     print(
         f"""P{self.__maximum_claim_table.index(UpperCAmelCase__							) + 1}"""
         + """ """.join(f"""{it:>8}""" for it in item							)
         + """\n"""							)
   print(
       """Current Usage by Active Processes: """
       + """ """.join(str(UpperCAmelCase__							) for x in self.__claim_vector							)							)
   print(
       """Initial Available Resources:       """
       + """ """.join(str(UpperCAmelCase__							) for x in self.__available_resources()							)							)
   time.sleep(1							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

