





"""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 		lowercase					:





      def __init__(       self      ,						lowercase = "cpu"      ,						lowercase = "openai/clip-vit-large-patch14" )		->     None:
            lowerCAmelCase  						=       device
            lowerCAmelCase  						=       CLIPTokenizerFast.from_pretrained(lowercase )
            lowerCAmelCase  						=       [0.48_145_466, 0.4_578_275, 0.40_821_073]
            lowerCAmelCase  						=       [0.26_862_954, 0.26_130_258, 0.27_577_711]
            lowerCAmelCase  						=       torchvision.transforms.Normalize(self.image_mean      ,						self.image_std )
            lowerCAmelCase  						=       torchvision.transforms.Resize(224 )
            lowerCAmelCase  						=       torchvision.transforms.CenterCrop(224 )





      def     _snake_case			(       self      ,						lowercase )		->     Dict:
            lowerCAmelCase  						=       self.resize(lowercase )
            lowerCAmelCase  						=       self.center_crop(lowercase )
            lowerCAmelCase  						=       self.normalize(lowercase )
            return images





      def __call__(       self      ,						lowercase=None      ,						lowercase=None      ,						**lowercase )		->     List[str]:
            lowerCAmelCase  						=       self.tokenizer(text=lowercase      ,						**lowercase )
            lowerCAmelCase  						=       self.preprocess_img(lowercase )
            lowerCAmelCase  						=       {key: value.to(self.device ) for (key, value) in encoding.items()}
            return encoding







class 		lowercase					(					nn.Module					):





      def __init__(       self      ,						lowercase=10      ,						lowercase=0.01      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=False      ,						lowercase=True      ,						lowercase="image"      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						)		->     None:
            super().__init__()
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       device if device else get_device()
            if vqgan:
                  lowerCAmelCase  						=       vqgan
            else:
                  lowerCAmelCase  						=       load_vqgan(self.device      ,						conf_path=lowercase      ,						ckpt_path=lowercase )
            self.vqgan.eval()
            if clip:
                  lowerCAmelCase  						=       clip
            else:
                  lowerCAmelCase  						=       CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""" )
            self.clip.to(self.device )
            lowerCAmelCase  						=       ProcessorGradientFlow(device=self.device )

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





      def     _snake_case			(       self      ,						lowercase=None      ,						lowercase=None      ,						lowercase=5      ,						lowercase=True )		->     Optional[int]:
            lowerCAmelCase  						=       []
            if output_path is None:
                  lowerCAmelCase  						=       """./animation.gif"""
            if input_path is None:
                  lowerCAmelCase  						=       self.save_path
            lowerCAmelCase  						=       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?)""" )
            lowerCAmelCase  						=       total_duration / len(lowercase )
            lowerCAmelCase  						=       [frame_duration] * len(lowercase )
            if extend_frames:
                  lowerCAmelCase  						=       1.5
                  lowerCAmelCase  						=       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     _snake_case			(       self      ,						lowercase=None      ,						lowercase=None )		->     str:
            if not (path or img):
                  raise ValueError("""Input either path or tensor""" )
            if img is not None:
                  raise NotImplementedError
            lowerCAmelCase  						=       preprocess(Image.open(lowercase )      ,						target_image_size=256 ).to(self.device )
            lowerCAmelCase  						=       preprocess_vqgan(lowercase )
            lowerCAmelCase						,		*lowerCAmelCase  						=       self.vqgan.encode(lowercase )
            return z





      def     _snake_case			(       self      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       self.latent.detach().requires_grad_()
            lowerCAmelCase  						=       base_latent + transform_vector
            if self.quantize:
                  lowerCAmelCase						,		*lowerCAmelCase  						=       self.vqgan.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       trans_latent
            return self.vqgan.decode(lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=None )		->     Tuple:
            lowerCAmelCase  						=       self.clip_preprocessor(text=lowercase      ,						images=lowercase      ,						return_tensors="""pt"""      ,						padding=lowercase )
            lowerCAmelCase  						=       self.clip(**lowercase )
            lowerCAmelCase  						=       clip_outputs.logits_per_image
            if weights is not None:
                  lowerCAmelCase  						=       similarity_logits * weights
            return similarity_logits.sum()





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Optional[Any]:
            lowerCAmelCase  						=       self._get_clip_similarity(pos_prompts["""prompts"""]      ,						lowercase      ,						weights=(1 / pos_prompts["""weights"""]) )
            if neg_prompts:
                  lowerCAmelCase  						=       self._get_clip_similarity(neg_prompts["""prompts"""]      ,						lowercase      ,						weights=neg_prompts["""weights"""] )
            else:
                  lowerCAmelCase  						=       torch.tensor([1]      ,						device=self.device )
            lowerCAmelCase  						=       -torch.log(lowercase ) + torch.log(lowercase )
            return loss





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Dict:
            lowerCAmelCase  						=       torch.randn_like(self.latent      ,						requires_grad=lowercase      ,						device=self.device )
            lowerCAmelCase  						=       torch.optim.Adam([vector]      ,						lr=self.lr )

            for i in range(self.iterations ):
                  optim.zero_grad()
                  lowerCAmelCase  						=       self._add_vector(lowercase )
                  lowerCAmelCase  						=       loop_post_process(lowercase )
                  lowerCAmelCase  						=       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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[str]:
            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:
                  lowerCAmelCase  						=       Image.open(lowercase )
                  lowerCAmelCase  						=       image.resize((256, 256) )
                  wandb.log("""Original Image"""      ,						wandb.Image(lowercase ) )





      def     _snake_case			(       self      ,						lowercase )		->     List[str]:
            if not prompts:
                  return []
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       []
            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       [prompt.strip() for prompt in prompts.split("""|""" )]
            for prompt in prompts:
                  if isinstance(lowercase      ,						(tuple, list) ):
                        lowerCAmelCase  						=       prompt[0]
                        lowerCAmelCase  						=       float(prompt[1] )
                  elif ":" in prompt:
                        lowerCAmelCase						,		lowerCAmelCase  						=       prompt.split(""":""" )
                        lowerCAmelCase  						=       float(lowercase )
                  else:
                        lowerCAmelCase  						=       prompt
                        lowerCAmelCase  						=       1.0
                  processed_prompts.append(lowercase )
                  weights.append(lowercase )
            return {
                "prompts": processed_prompts,
                "weights": torch.tensor(lowercase      ,						device=self.device ),
            }





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=None      ,						lowercase=True      ,						lowercase=False      ,						lowercase=True      ,						lowercase=True      ,						lowercase=None      ,						)		->     Any:
            if image_path:
                  lowerCAmelCase  						=       self._get_latent(lowercase )
            else:
                  lowerCAmelCase  						=       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."
            lowerCAmelCase  						=       self.process_prompts(lowercase )
            lowerCAmelCase  						=       self.process_prompts(lowercase )
            if save_final and save_path is None:
                  lowerCAmelCase  						=       os.path.join("""./outputs/"""      ,						"""_""".join(pos_prompts["""prompts"""] ) )
            if not os.path.exists(lowercase ):
                  os.makedirs(lowercase )
            else:
                  lowerCAmelCase  						=       save_path + """_""" + get_timestamp()
                  os.makedirs(lowercase )
            lowerCAmelCase  						=       save_path

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

            lowerCAmelCase  						=       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' ) )










'''simple docstring'''


class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case = ""    ,					__snake_case = False  ):
											# Mapping from the first character of the prefix of the node
											_SCREAMING_SNAKE_CASE :       dict[str, RadixNode]     		=      {}

											# A node will be a leaf if the tree contains its word
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      is_leaf

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prefix






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      0
											for q, w in zip(self.prefix    ,					__snake_case  ):
															if q != w:
																			break

															x += 1

											return self.prefix[:x], self.prefix[x:], word[x:]






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											for word in words:
															self.insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# Case 1: If the word is the prefix of the node
											# Solution: We set the current node as leaf
											if self.prefix == word:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      True

											# Case 2: The node has no edges that have a prefix to the word
											# Solution: We create an edge from the current node to a new one
											# containing the word
											elif word[0] not in self.nodes:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      RadixNode(prefix=__snake_case    ,					is_leaf=__snake_case  )

											else:
															_SCREAMING_SNAKE_CASE :       int     		=      self.nodes[word[0]]
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      incoming_node.match(
															    __snake_case  )

															# Case 3: The node prefix is equal to the matching
															# Solution: We insert remaining word on the next node
															if remaining_prefix == "":
																			self.nodes[matching_string[0]].insert(__snake_case  )

															# Case 4: The word is greater equal to the matching
															# Solution: Create a node in between both nodes, change
															# prefixes and add the new node for the remaining word
															else:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      remaining_prefix

																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes[matching_string[0]]
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      RadixNode(__snake_case    ,					__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      aux_node

																			if remaining_word == "":
																							_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      True
																			else:
																							self.nodes[matching_string[0]].insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      incoming_node.match(
															    __snake_case  )
															# If there is remaining prefix, the word can't be on the tree
															if remaining_prefix != "":
																			return False
															# This applies when the word and the prefix are equal
															elif remaining_word == "":
																			return incoming_node.is_leaf
															# We have word remaining so we check the next node
															else:
																			return incoming_node.find(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      incoming_node.match(
															    __snake_case  )
															# If there is remaining prefix, the word can't be on the tree
															if remaining_prefix != "":
																			return False
															# We have word remaining so we check the next node
															elif remaining_word != "":
																			return incoming_node.delete(__snake_case  )
															else:
																			# If it is not a leaf, we don't have to delete
																			if not incoming_node.is_leaf:
																							return False
																			else:
																							# We delete the nodes if no edges go from it
																							if len(incoming_node.nodes  ) == 0:
																											del self.nodes[word[0]]
																											# We merge the current node with its only child
																											if len(self.nodes  ) == 1 and not self.is_leaf:
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(self.nodes.values()  )[0]
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      merging_node.is_leaf
																															self.prefix += merging_node.prefix
																															_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																							elif len(incoming_node.nodes  ) > 1:
																											_SCREAMING_SNAKE_CASE :       List[str]     		=      False
																							# If there is 1 edge, we merge it with its child
																							else:
																											_SCREAMING_SNAKE_CASE :       int     		=      list(incoming_node.nodes.values()  )[0]
																											_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											_SCREAMING_SNAKE_CASE :       str     		=      merging_node.nodes

																							return True






							def UpperCAmelCase_	(    self    ,					__snake_case = 0  ):
											if self.prefix != "":
															print("""-""" * height    ,					self.prefix    ,					"""  (leaf)""" if self.is_leaf else """"""  )

											for value in self.nodes.values():
															value.print_tree(height + 1  )







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """banana bananas bandana band apple all beast""".split()
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      RadixNode()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				assert all(root.find(SCREAMING_SNAKE_CASE__     ) for word in words     )
				assert not root.find("""bandanas"""     )
				assert not root.find("""apps"""     )
				root.delete("""all"""     )
				assert not root.find("""all"""     )
				root.delete("""banana"""     )
				assert not root.find("""banana"""     )
				assert root.find("""bananas"""     )

				return True







def 			snake_case_   (  ):







				"""simple docstring"""





				assert test_trie()







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      RadixNode()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				print("""Words:"""							,      SCREAMING_SNAKE_CASE__     )
				print("""Tree:"""     )
				root.print_tree()


if __name__ == "__main__":
				main()




