





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






     '''simple docstring'''





     lowercase__			       =  device
     lowercase__			       =  CLIPTokenizerFast.from_pretrained(_lowercase )
     lowercase__			       =  [0.48145466, 0.4578275, 0.40821073]
     lowercase__			       =  [0.26862954, 0.26130258, 0.27577711]
     lowercase__			       =  torchvision.transforms.Normalize(self.image_mean   ,							self.image_std )
     lowercase__			       =  torchvision.transforms.Resize(2_24 )
     lowercase__			       =  torchvision.transforms.CenterCrop(2_24 )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.resize(_lowercase )
     lowercase__			       =  self.center_crop(_lowercase )
     lowercase__			       =  self.normalize(_lowercase )
     return images







  def __call__(      self  :List[Any]   ,							_lowercase  :Optional[int]=None   ,							_lowercase  :Optional[int]=None   ,							**_lowercase  :List[Any] ):






     '''simple docstring'''





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





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Optional[Any]   ,							_lowercase  :List[Any]=10   ,							_lowercase  :int=0.01   ,							_lowercase  :List[str]=None   ,							_lowercase  :List[str]=None   ,							_lowercase  :int=None   ,							_lowercase  :Tuple=None   ,							_lowercase  :Union[str, Any]=None   ,							_lowercase  :Optional[int]=None   ,							_lowercase  :List[str]=False   ,							_lowercase  :List[Any]=True   ,							_lowercase  :Optional[Any]="image"   ,							_lowercase  :str=True   ,							_lowercase  :int=False   ,							_lowercase  :Dict=False   ,							_lowercase  :Optional[Any]=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=_lowercase   ,							ckpt_path=_lowercase )
     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  :List[Any]   ,							_lowercase  :Union[str, Any]=None   ,							_lowercase  :Optional[Any]=None   ,							_lowercase  :List[str]=5   ,							_lowercase  :str=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(_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?)" )
     lowercase__			       =  total_duration / len(_lowercase )
     lowercase__			       =  [frame_duration] * len(_lowercase )
     if extend_frames:
        lowercase__			       =  1.5
        lowercase__			       =  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   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[Any]=None   ,							_lowercase  :Dict=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(_lowercase )   ,							target_image_size=2_56 ).to(self.device )
     lowercase__			       =  preprocess_vqgan(_lowercase )
     lowercase__       ,							*lowercase__			       =  self.vqgan.encode(_lowercase )
     return z







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :str ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Any   ,							_lowercase  :Optional[int]   ,							_lowercase  :Union[str, Any]=None ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :int   ,							_lowercase  :Dict   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Tuple   ,							_lowercase  :Any ):






     '''simple docstring'''





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

     for i in range(self.iterations ):
        optim.zero_grad()
        lowercase__			       =  self._add_vector(_lowercase )
        lowercase__			       =  loop_post_process(_lowercase )
        lowercase__			       =  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   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :Any   ,							_lowercase  :List[str] ):






     '''simple docstring'''





     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:
        lowercase__			       =  Image.open(_lowercase )
        lowercase__			       =  image.resize((2_56, 2_56) )
        wandb.log("Original Image"   ,							wandb.Image(_lowercase ) )







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :List[str] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :Dict   ,							_lowercase  :Tuple=None   ,							_lowercase  :List[Any]=None   ,							_lowercase  :Optional[int]=True   ,							_lowercase  :str=False   ,							_lowercase  :Dict=True   ,							_lowercase  :int=True   ,							_lowercase  :List[str]=None   ,							):






     '''simple docstring'''





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

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

     lowercase__			       =  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''' ) )













import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_snake_case           =			logging.getLogger(__name__)





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,							)
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Whether tp freeze the encoder.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Whether  to freeze the embeddings.'} )





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
  __lowerCamelCase		=      field(
      default='summarization'							,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}							,							)
  __lowerCamelCase		=      field(
      default=1_024							,							metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      field(
      default=128							,							metadata={
          'help': (
              'The maximum total sequence length for target text after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      field(
      default=142							,							metadata={
          'help': (
              'The maximum total sequence length for validation target text after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded. '
              'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
              'during ``evaluate`` and ``predict``.'
          )
      }							,							)
  __lowerCamelCase		=      field(
      default=142							,							metadata={
          'help': (
              'The maximum total sequence length for test target text after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# training examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# validation examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# test examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Source language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Target language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': '# num_beams to use for evaluation.'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}							,							)


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   logger.info(f'''***** {split} metrics *****'''	)
   for key in sorted(metrics.keys()	):
      logger.info(f'''  {key} = {metrics[key]}'''	)
   save_json(__magic_name__  , os.path.join(__magic_name__  , f'''{split}_results.json'''	)	)


def _A       (   ):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowercase__			       =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)	)

   if len(sys.argv	) == 2 and sys.argv[1].endswith(".json"	):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
   else:
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_args_into_dataclasses()

   check_output_dir(__magic_name__	)

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  , datefmt="%m/%d/%Y %H:%M:%S"  , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  , )
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"  , training_args.local_rank  , training_args.device  , training_args.n_gpu  , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED	)  , training_args.fpaa  , )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank	):
      transformers.utils.logging.set_verbosity_info()
   logger.info("Training/evaluation parameters %s"  , __magic_name__	)

   # Set seed
   set_seed(training_args.seed	)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   lowercase__			       =  AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path  , cache_dir=model_args.cache_dir  , )

   lowercase__			       =  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(__magic_name__  , __magic_name__  , __magic_name__	):
         assert hasattr(__magic_name__  , __magic_name__	), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
         setattr(__magic_name__  , __magic_name__  , getattr(__magic_name__  , __magic_name__	)	)

   lowercase__			       =  AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  , cache_dir=model_args.cache_dir  , )
   lowercase__			       =  AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path  , from_tf=".ckpt" in model_args.model_name_or_path  , config=__magic_name__  , cache_dir=model_args.cache_dir  , )

   # use task specific params
   use_task_specific_params(__magic_name__  , data_args.task	)

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowercase__			       =  model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(__magic_name__  , (MBartTokenizer, MBartTokenizerFast)	):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(__magic_name__  , __magic_name__	):
         lowercase__			       =  tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowercase__			       =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

   if model_args.freeze_embeds:
      freeze_embeds(__magic_name__	)
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()	)
      assert_all_frozen(model.get_encoder()	)

   lowercase__			       =  SeqaSeqDataset

   # Get datasets
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , type_path="train"  , data_dir=data_args.data_dir  , n_obs=data_args.n_train  , max_target_length=data_args.max_target_length  , max_source_length=data_args.max_source_length  , prefix=model.config.prefix or ""  , )
       if training_args.do_train
       else None
   )
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , type_path="val"  , data_dir=data_args.data_dir  , n_obs=data_args.n_val  , max_target_length=data_args.val_max_target_length  , max_source_length=data_args.max_source_length  , prefix=model.config.prefix or ""  , )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , type_path="test"  , data_dir=data_args.data_dir  , n_obs=data_args.n_test  , max_target_length=data_args.test_max_target_length  , max_source_length=data_args.max_source_length  , prefix=model.config.prefix or ""  , )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowercase__			       =  (
       build_compute_metrics_fn(data_args.task  , __magic_name__	) if training_args.predict_with_generate else None
   )
   lowercase__			       =  SeqaSeqTrainer(
       model=__magic_name__  , args=__magic_name__  , data_args=__magic_name__  , train_dataset=__magic_name__  , eval_dataset=__magic_name__  , data_collator=SeqaSeqDataCollator(
           __magic_name__  , __magic_name__  , model.config.decoder_start_token_id  , training_args.tpu_num_cores	)  , compute_metrics=__magic_name__  , tokenizer=__magic_name__  , )

   lowercase__			       =  {}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"	)

      lowercase__			       =  trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
      lowercase__			       =  train_result.metrics
      lowercase__			       =  data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir  , "trainer_state.json"	)	)

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"	)

      lowercase__			       =  trainer.evaluate(metric_key_prefix="val"	)
      lowercase__			       =  data_args.n_val
      lowercase__			       =  round(metrics["val_loss"]  , 4	)

      if trainer.is_world_process_zero():
         handle_metrics("val"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

   if training_args.do_predict:
      logger.info("*** Predict ***"	)

      lowercase__			       =  trainer.predict(test_dataset=__magic_name__  , metric_key_prefix="test"	)
      lowercase__			       =  test_output.metrics
      lowercase__			       =  data_args.n_test

      if trainer.is_world_process_zero():
         lowercase__			       =  round(metrics["test_loss"]  , 4	)
         handle_metrics("test"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

         if training_args.predict_with_generate:
            lowercase__			       =  tokenizer.batch_decode(
                test_output.predictions  , skip_special_tokens=__magic_name__  , clean_up_tokenization_spaces=__magic_name__	)
            lowercase__			       =  lmap(str.strip  , __magic_name__	)
            write_txt_file(__magic_name__  , os.path.join(training_args.output_dir  , "test_generations.txt"	)	)

   if trainer.is_world_process_zero():
      save_json(__magic_name__  , os.path.join(training_args.output_dir  , "all_results.json"	)	)

   return all_metrics


def _A       (   __magic_name__	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






