
"""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 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = "cpu"  ,     snake_case__ = "openai/clip-vit-large-patch14"				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = device
       _SCREAMING_SNAKE_CASE   :				List[str]							    = CLIPTokenizerFast.from_pretrained(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = [0.48_145_466, 0.4_578_275, 0.40_821_073]
       _SCREAMING_SNAKE_CASE   :				List[str]							    = [0.26_862_954, 0.26_130_258, 0.27_577_711]
       _SCREAMING_SNAKE_CASE   :				Any							    = torchvision.transforms.Normalize(self.image_mean  ,     self.image_std				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = torchvision.transforms.Resize(224				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = torchvision.transforms.CenterCrop(224				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.resize(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.center_crop(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.normalize(__UpperCamelCase				)
       return images




     def __call__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.tokenizer(text=__UpperCamelCase  ,     **__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.preprocess_img(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				str							    = {key: value.to(self.device				) for (key, value) in encoding.items()}
       return encoding
class 			UpperCamelCase      (							nn.Module  ):
     def __init__(     self  ,     snake_case__=10  ,     snake_case__=0.01  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=False  ,     snake_case__=True  ,     snake_case__="image"  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__=False  ,     snake_case__=False  ,     ):




       """simple docstring"""


       super().__init__()
       _SCREAMING_SNAKE_CASE   :				Tuple							    = None
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = device if device else get_device()
       if vqgan:
         _SCREAMING_SNAKE_CASE   :				Dict							    = vqgan
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = load_vqgan(self.device  ,     conf_path=__UpperCamelCase  ,     ckpt_path=__UpperCamelCase				)
       self.vqgan.eval()
       if clip:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = clip
       else:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = CLIPModel.from_pretrained("openai/clip-vit-base-patch32"				)
       self.clip.to(self.device				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = ProcessorGradientFlow(device=self.device				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = iterations
       _SCREAMING_SNAKE_CASE   :				List[str]							    = lr
       _SCREAMING_SNAKE_CASE   :				Any							    = log
       _SCREAMING_SNAKE_CASE   :				Dict							    = make_grid
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = return_val
       _SCREAMING_SNAKE_CASE   :				Any							    = quantize
       _SCREAMING_SNAKE_CASE   :				int							    = self.vqgan.decoder.z_shape
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=5  ,     snake_case__=True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
       if output_path is None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "./animation.gif"
       if input_path is None:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self.save_path
       _SCREAMING_SNAKE_CASE   :				Tuple							    = 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?)"				)
       _SCREAMING_SNAKE_CASE   :				Any							    = total_duration / len(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Any							    = [frame_duration] * len(__UpperCamelCase				)
       if extend_frames:
         _SCREAMING_SNAKE_CASE   :				Dict							    = 1.5
         _SCREAMING_SNAKE_CASE   :				Dict							    = 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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None  ,     snake_case__=None				):




       """simple docstring"""


       if not (path or img):
         raise ValueError("Input either path or tensor"				)
       if img is not None:
         raise NotImplementedError
       _SCREAMING_SNAKE_CASE   :				str							    = preprocess(Image.open(__UpperCamelCase				)  ,     target_image_size=256				).to(self.device				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = preprocess_vqgan(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE    ,		*_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.vqgan.encode(__UpperCamelCase				)
       return z
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.latent.detach().requires_grad_()
       _SCREAMING_SNAKE_CASE   :				List[str]							    = base_latent + transform_vector
       if self.quantize:
         _SCREAMING_SNAKE_CASE    ,		*_SCREAMING_SNAKE_CASE   :				Any							    = self.vqgan.quantize(__UpperCamelCase				)
       else:
         _SCREAMING_SNAKE_CASE   :				Dict							    = trans_latent
       return self.vqgan.decode(__UpperCamelCase				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__=None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.clip_preprocessor(text=__UpperCamelCase  ,     images=__UpperCamelCase  ,     return_tensors="pt"  ,     padding=__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.clip(**__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = clip_outputs.logits_per_image
       if weights is not None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = similarity_logits * weights
       return similarity_logits.sum()
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self._get_clip_similarity(pos_prompts["prompts"]  ,     __UpperCamelCase  ,     weights=(1 / pos_prompts["weights"])				)
       if neg_prompts:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self._get_clip_similarity(neg_prompts["prompts"]  ,     __UpperCamelCase  ,     weights=neg_prompts["weights"]				)
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = torch.tensor([1]  ,     device=self.device				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = -torch.log(__UpperCamelCase				) + torch.log(__UpperCamelCase				)
       return loss
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = torch.randn_like(self.latent  ,     requires_grad=__UpperCamelCase  ,     device=self.device				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = torch.optim.Adam([vector]  ,     lr=self.lr				)

       for i in range(self.iterations				):
         optim.zero_grad()
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self._add_vector(__UpperCamelCase				)
         _SCREAMING_SNAKE_CASE   :				int							    = loop_post_process(__UpperCamelCase				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = 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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """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:
         _SCREAMING_SNAKE_CASE   :				str							    = Image.open(__UpperCamelCase				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = image.resize((256, 256)				)
         wandb.log("Original Image"  ,     wandb.Image(__UpperCamelCase				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


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




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=None  ,     ):




       """simple docstring"""


       if image_path:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self._get_latent(__UpperCamelCase				)
       else:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = 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."
       _SCREAMING_SNAKE_CASE   :				int							    = self.process_prompts(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.process_prompts(__UpperCamelCase				)
       if save_final and save_path is None:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = os.path.join("./outputs/"  ,     "_".join(pos_prompts["prompts"]				)				)
       if not os.path.exists(__UpperCamelCase				):
         os.makedirs(__UpperCamelCase				)
       else:
         _SCREAMING_SNAKE_CASE   :				Dict							    = save_path + "_" + get_timestamp()
         os.makedirs(__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				str							    = save_path

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.vqgan.decode(self.latent				)[0]
       if show_intermediate:
         print("Original Image"				)
         show_pil(custom_to_pil(__UpperCamelCase				)				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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"""
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowercase_       :						List[str]     					=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 			UpperCamelCase      (							datasets.BuilderConfig  ):
     A__      =			10000
     A__      =			None
     A__      =			None
class 			UpperCamelCase      (							datasets.ArrowBasedBuilder  ):
     A__      =			ParquetConfig
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.DatasetInfo(features=self.config.features				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if not self.config.data_files:
         raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = dl_manager.download_and_extract(self.config.data_files				)
       if isinstance(snake_case__  ,     (str, list, tuple)				):
         _SCREAMING_SNAKE_CASE   :				int							    = data_files
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [dl_manager.iter_files(snake_case__				) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,     gen_kwargs={"files": files}				)]
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       for split_name, files in data_files.items():
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [dl_manager.iter_files(snake_case__				) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(snake_case__				):
             with open(snake_case__  ,     "rb"				) as f:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = datasets.Features.from_arrow_schema(pq.read_schema(snake_case__				)				)
             break
         splits.append(datasets.SplitGenerator(name=snake_case__  ,     gen_kwargs={"files": files}				)				)
       return splits
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = table_cast(snake_case__  ,     self.info.features.arrow_schema				)
       return pa_table




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema				) != sorted(self.config.columns				):
           raise ValueError(
               F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''				)
       for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
         with open(snake_case__  ,     "rb"				) as f:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = pq.ParquetFile(snake_case__				)
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size  ,     columns=self.config.columns				)				):
               _SCREAMING_SNAKE_CASE   :				int							    = pa.Table.from_batches([record_batch]				)
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F'''{file_idx}_{batch_idx}''', self._cast_table(snake_case__				)
           except ValueError as e:
             logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case__				)}: {e}'''				)
             raise



