



'''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 :






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					str = "cpu"  ,						UpperCamelCase__      :					str = "openai/clip-vit-large-patch14" ):


       """simple docstring"""







       UpperCamelCase					=  device
       UpperCamelCase					=  CLIPTokenizerFast.from_pretrained(UpperCamelCase__ )
       UpperCamelCase					=  [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
       UpperCamelCase					=  [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
       UpperCamelCase					=  torchvision.transforms.Normalize(self.image_mean  ,						self.image_std )
       UpperCamelCase					=  torchvision.transforms.Resize(2_2_4 )
       UpperCamelCase					=  torchvision.transforms.CenterCrop(2_2_4 )






    def  A       (  self      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.resize(UpperCamelCase__ )
       UpperCamelCase					=  self.center_crop(UpperCamelCase__ )
       UpperCamelCase					=  self.normalize(UpperCamelCase__ )
       return images






    def __call__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					Optional[Any]=None  ,						**UpperCamelCase__      :					Any ):


       """simple docstring"""







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

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Optional[int]=1_0  ,						UpperCamelCase__      :					Optional[int]=0.0_1  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					Optional[Any]=None  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					Dict=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					List[str]=False  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					List[str]="image"  ,						UpperCamelCase__      :					Union[str, Any]=True  ,						UpperCamelCase__      :					Dict=False  ,						UpperCamelCase__      :					List[Any]=False  ,						UpperCamelCase__      :					Dict=False  ,						):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  None
       UpperCamelCase					=  device if device else get_device()
       if vqgan:
          UpperCamelCase					=  vqgan
       else:
          UpperCamelCase					=  load_vqgan(self.device  ,						conf_path=UpperCamelCase__  ,						ckpt_path=UpperCamelCase__ )
       self.vqgan.eval()
       if clip:
          UpperCamelCase					=  clip
       else:
          UpperCamelCase					=  CLIPModel.from_pretrained('openai/clip-vit-base-patch32' )
       self.clip.to(self.device )
       UpperCamelCase					=  ProcessorGradientFlow(device=self.device )

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






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					List[str]=True ):


       """simple docstring"""







       UpperCamelCase					=  []
       if output_path is None:
          UpperCamelCase					=  './animation.gif'
       if input_path is None:
          UpperCamelCase					=  self.save_path
       UpperCamelCase					=  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?)' )
       UpperCamelCase					=  total_duration / len(UpperCamelCase__ )
       UpperCamelCase					=  [frame_duration] * len(UpperCamelCase__ )
       if extend_frames:
          UpperCamelCase					=  1.5
          UpperCamelCase					=  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  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					List[Any]=None  ,						UpperCamelCase__      :					Optional[Any]=None ):


       """simple docstring"""







       if not (path or img):
          raise ValueError('Input either path or tensor' )
       if img is not None:
          raise NotImplementedError
       UpperCamelCase					=  preprocess(Image.open(UpperCamelCase__ )  ,						target_image_size=2_5_6 ).to(self.device )
       UpperCamelCase					=  preprocess_vqgan(UpperCamelCase__ )
       UpperCamelCase				,      *UpperCamelCase					=  self.vqgan.encode(UpperCamelCase__ )
       return z






    def  A       (  self      :					str  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







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






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None ):


       """simple docstring"""







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






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







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






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







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

       for i in range(self.iterations ):
          optim.zero_grad()
          UpperCamelCase					=  self._add_vector(UpperCamelCase__ )
          UpperCamelCase					=  loop_post_process(UpperCamelCase__ )
          UpperCamelCase					=  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  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					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:
          UpperCamelCase					=  Image.open(UpperCamelCase__ )
          UpperCamelCase					=  image.resize((2_5_6, 2_5_6) )
          wandb.log('Original Image'  ,						wandb.Image(UpperCamelCase__ ) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







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






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					List[str]=False  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=None  ,						):


       """simple docstring"""







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

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

       UpperCamelCase					=  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 json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

   with open(A__    ,						'wb'    ) as f:
      f.write(json.dumps(A__    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()

