




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      ,				__UpperCAmelCase = "cpu"      ,				__UpperCAmelCase = "openai/clip-vit-large-patch14"		):
        lowerCAmelCase__				:  Union[str, Any]		       =     device
        lowerCAmelCase__				:  Optional[int]		       =     CLIPTokenizerFast.from_pretrained(__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     [0.48145466, 0.4578275, 0.40821073]
        lowerCAmelCase__				:  Optional[Any]		       =     [0.26862954, 0.26130258, 0.27577711]
        lowerCAmelCase__				:  Optional[Any]		       =     torchvision.transforms.Normalize(self.image_mean      ,				self.image_std		)
        lowerCAmelCase__				:  Dict		       =     torchvision.transforms.Resize(224		)
        lowerCAmelCase__				:  Any		       =     torchvision.transforms.CenterCrop(224		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.resize(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     self.center_crop(__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     self.normalize(__UpperCAmelCase		)
        return images







      def __call__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     self.tokenizer(text=__UpperCAmelCase      ,				**__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     self.preprocess_img(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     {key: value.to(self.device		) for (key, value) in encoding.items()}
        return encoding






class  _lowerCAmelCase     (  nn.Module	):

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

        lowerCAmelCase__				:  Tuple		       =     iterations
        lowerCAmelCase__				:  Tuple		       =     lr
        lowerCAmelCase__				:  Optional[int]		       =     log
        lowerCAmelCase__				:  Dict		       =     make_grid
        lowerCAmelCase__				:  Optional[Any]		       =     return_val
        lowerCAmelCase__				:  List[Any]		       =     quantize
        lowerCAmelCase__				:  List[str]		       =     self.vqgan.decoder.z_shape

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=5      ,				__UpperCAmelCase=True		):
        lowerCAmelCase__				:  Union[str, Any]		       =     []
        if output_path is None:
          lowerCAmelCase__				:  Optional[Any]		       =     '''./animation.gif'''
        if input_path is None:
          lowerCAmelCase__				:  List[Any]		       =     self.save_path
        lowerCAmelCase__				:  List[str]		       =     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?)'''		)
        lowerCAmelCase__				:  Optional[Any]		       =     total_duration / len(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     [frame_duration] * len(__UpperCAmelCase		)
        if extend_frames:
          lowerCAmelCase__				:  Any		       =     1.5
          lowerCAmelCase__				:  Optional[int]		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None		):
        if not (path or img):
          raise ValueError('''Input either path or tensor'''		)
        if img is not None:
          raise NotImplementedError
        lowerCAmelCase__				:  int		       =     preprocess(Image.open(__UpperCAmelCase		)      ,				target_image_size=256		).to(self.device		)
        lowerCAmelCase__				:  Any		       =     preprocess_vqgan(__UpperCAmelCase		)
        lowerCAmelCase__       ,			*lowerCAmelCase__				:  Dict		       =     self.vqgan.encode(__UpperCAmelCase		)
        return z

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__				:  List[str]		       =     self.clip_preprocessor(text=__UpperCAmelCase      ,				images=__UpperCAmelCase      ,				return_tensors='''pt'''      ,				padding=__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     self.clip(**__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     clip_outputs.logits_per_image
        if weights is not None:
          lowerCAmelCase__				:  Dict		       =     similarity_logits * weights
        return similarity_logits.sum()

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     self._get_clip_similarity(pos_prompts['''prompts''']      ,				__UpperCAmelCase      ,				weights=(1 / pos_prompts['''weights'''])		)
        if neg_prompts:
          lowerCAmelCase__				:  Union[str, Any]		       =     self._get_clip_similarity(neg_prompts['''prompts''']      ,				__UpperCAmelCase      ,				weights=neg_prompts['''weights''']		)
        else:
          lowerCAmelCase__				:  Union[str, Any]		       =     torch.tensor([1]      ,				device=self.device		)
        lowerCAmelCase__				:  Tuple		       =     -torch.log(__UpperCAmelCase		) + torch.log(__UpperCAmelCase		)
        return loss

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     torch.randn_like(self.latent      ,				requires_grad=__UpperCAmelCase      ,				device=self.device		)
        lowerCAmelCase__				:  List[Any]		       =     torch.optim.Adam([vector]      ,				lr=self.lr		)

        for i in range(self.iterations		):
          optim.zero_grad()
          lowerCAmelCase__				:  Union[str, Any]		       =     self._add_vector(__UpperCAmelCase		)
          lowerCAmelCase__				:  List[str]		       =     loop_post_process(__UpperCAmelCase		)
          lowerCAmelCase__				:  Tuple		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        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:
          lowerCAmelCase__				:  int		       =     Image.open(__UpperCAmelCase		)
          lowerCAmelCase__				:  int		       =     image.resize((256, 256)		)
          wandb.log('''Original Image'''      ,				wandb.Image(__UpperCAmelCase		)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        if not prompts:
          return []
        lowerCAmelCase__				:  Dict		       =     []
        lowerCAmelCase__				:  Tuple		       =     []
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     [prompt.strip() for prompt in prompts.split('''|'''		)]
        for prompt in prompts:
          if isinstance(__UpperCAmelCase      ,				(tuple, list)		):
            lowerCAmelCase__				:  Dict		       =     prompt[0]
            lowerCAmelCase__				:  Optional[int]		       =     float(prompt[1]		)
          elif ":" in prompt:
            lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     prompt.split(''':'''		)
            lowerCAmelCase__				:  Any		       =     float(__UpperCAmelCase		)
          else:
            lowerCAmelCase__				:  int		       =     prompt
            lowerCAmelCase__				:  Union[str, Any]		       =     1.0
          processed_prompts.append(__UpperCAmelCase		)
          weights.append(__UpperCAmelCase		)
        return {
            "prompts": processed_prompts,
            "weights": torch.tensor(__UpperCAmelCase      ,				device=self.device		),
        }







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=None      ,				):
        if image_path:
          lowerCAmelCase__				:  Union[str, Any]		       =     self._get_latent(__UpperCAmelCase		)
        else:
          lowerCAmelCase__				:  Union[str, Any]		       =     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."
        lowerCAmelCase__				:  Optional[Any]		       =     self.process_prompts(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     self.process_prompts(__UpperCAmelCase		)
        if save_final and save_path is None:
          lowerCAmelCase__				:  List[Any]		       =     os.path.join('''./outputs/'''      ,				'''_'''.join(pos_prompts['''prompts''']		)		)
        if not os.path.exists(__UpperCAmelCase		):
          os.makedirs(__UpperCAmelCase		)
        else:
          lowerCAmelCase__				:  int		       =     save_path + '''_''' + get_timestamp()
          os.makedirs(__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     save_path

        lowerCAmelCase__				:  Dict		       =     self.vqgan.decode(self.latent		)[0]
        if show_intermediate:
          print('''Original Image'''		)
          show_pil(custom_to_pil(__UpperCAmelCase		)		)

        lowerCAmelCase__				:  Union[str, Any]		       =     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 collections import Counter
from timeit import timeit



def      __lowerCAmelCase			(					UpperCamelCase = ""	,      )     ->	bool:
  return sum(c % 2 for c in Counter(input_str.replace(''' '''	,      ''''''      ).lower()      ).values()      ) < 2



def      __lowerCAmelCase			(					UpperCamelCase = ""      )     ->	bool:
  if len(UpperCamelCase      ) == 0:
    return True
  lowerCAmelCase__				:  str		       =     input_str.replace(''' '''	,      ''''''      ).lower()
  # character_freq_dict: Stores the frequency of every character in the input string
  lowerCAmelCase__				:  dict[str, int]		       =     {}

  for character in lower_case_input_str:
    lowerCAmelCase__				:  int		       =     character_freq_dict.get(UpperCamelCase	,      0      ) + 1
  lowerCAmelCase__				:  Any		       =     0

  for character_count in character_freq_dict.values():
    if character_count % 2:
      odd_char += 1
  if odd_char > 1:
    return False
  return True



def      __lowerCAmelCase			(					UpperCamelCase = ""      )     ->	None:
  print('''\nFor string = '''	,      UpperCamelCase	,      ''':'''      )
  print(
      '''> can_string_be_rearranged_as_palindrome_counter()'''	,      '''\tans ='''	,      can_string_be_rearranged_as_palindrome_counter(UpperCamelCase      )	,      '''\ttime ='''	,      timeit(
          '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''	,      setup='''import __main__ as z'''	,      )	,      '''seconds'''	,      )
  print(
      '''> can_string_be_rearranged_as_palindrome()'''	,      '''\tans ='''	,      can_string_be_rearranged_as_palindrome(UpperCamelCase      )	,      '''\ttime ='''	,      timeit(
          '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''	,      setup='''import __main__ as z'''	,      )	,      '''seconds'''	,      )


if __name__ == "__main__":
     lowerCAmelCase_					    = input(
         """Enter string to determine if it can be rearranged as a palindrome or not: """
     ).strip()
     benchmark(check_str)
     lowerCAmelCase_					    = can_string_be_rearranged_as_palindrome_counter(check_str)
     print(F"""{check_str} can {"" if status else "not "}be rearranged as a palindrome""")

